human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "expander decomposition and pruning faster stronger and simpler"
  Abstract: We study the problem of graph clustering where the goal is to partition a graph into clusters, i.e. disjoint subsets of vertices, such that each cluster is well connected internally while sparsely connected to the rest of the graph. In particular, we use a natural bicriteria notion motivated by Kannan, Vempala, and Vetta which we refer to as {\em expander decomposition}. Expander decomposition has become one of the building blocks in the design of fast graph algorithms, most notably in the nearly linear time Laplacian solver by Spielman and Teng, and it also has wide applications in practice. #R##N#We design algorithm for the parametrized version of expander decomposition, where given a graph $G$ of $m$ edges and a parameter $\phi$, our algorithm finds a partition of the vertices into clusters such that each cluster induces a subgraph of conductance at least $\phi$ (i.e. a $\phi$ expander), and only a $\widetilde{O}(\phi)$ fraction of the edges in $G$ have endpoints across different clusters. Our algorithm runs in $\widetilde{O}(m/\phi)$ time, and is the first nearly linear time algorithm when $\phi$ is at least $1/\log^{O(1)} m$, which is the case in most practical settings and theoretical applications. Previous results either take $\Omega(m^{1+o(1)})$ time, or attain nearly linear time but with a weaker expansion guarantee where each output cluster is guaranteed to be contained inside some unknown $\phi$ expander. Our result achieve both nearly linear running time and the strong expander guarantee for clusters. Moreover, a main technique we develop for our result can be applied to obtain a much better \emph{expander pruning} algorithm, which is the key tool for maintaining an expander decomposition on dynamic graphs. Finally, we note that our algorithm is developed from first principles based on relatively simple and basic techniques, thus making it very likely to be practical.

Connected through hyperedge HE-1 (induced by source paper "coarse grained complexity for dynamic algorithms"):
  with members:
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "more consequences of falsifying seth and the orthogonal vectors conjecture"
    - "improved algorithms for fully dynamic maximal independent set"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic algorithms for graph coloring"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"

Connected through hyperedge HE-2 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"):
  with members:
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "distributed edge connectivity in sublinear time"
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "flows in almost linear time via adaptive preconditioning"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"

Connected through hyperedge HE-3 (induced by source paper "flows in almost linear time via adaptive preconditioning"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "computing maximum flow with augmenting electrical flows"

Connected through hyperedge HE-4 (induced by source paper "distributed edge connectivity in sublinear time"):
  with members:
    - "improved distributed expander decomposition and nearly optimal triangle enumeration"
    - "small cuts and connectivity certificates a fault tolerant approach"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"
    - "distributed exact weighted all pairs shortest paths in tilde o n 5 4 rounds"

Connected through hyperedge HE-5 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "distributed edge connectivity in sublinear time"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"
    - "local flow partitioning for faster edge connectivity"

Connected through hyperedge HE-6 (induced by source paper "improved distributed expander decomposition and nearly optimal triangle enumeration"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "sparse matrix multiplication and triangle listing in the congested clique model"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "distributed triangle detection via expander decomposition"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "mst in o 1 rounds of the congested clique"
    - "fooling views a new lower bound technique for distributed computations under congestion"
    - "triangle finding and listing in congest networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic low stretch trees via dynamic low diameter decompositions"):
  with members:
    - "faster distributed shortest path approximations via shortcuts"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "fully dynamic minimum spanning forest with improved worst case update time"
    - "on fully dynamic graph sparsifiers"
    - "fully dynamic spanners with worst case update time"

Connected through hyperedge HE-8 (induced by source paper "vertex sparsifiers for c edge connectivity"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "improved guarantees for vertex sparsification in planar graphs"
    - "the power of vertex sparsifiers in dynamic graph algorithms"
    - "sampling random spanning trees faster than matrix multiplication"
    - "on fully dynamic graph sparsifiers"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DS(Data Structures and Algorithms)