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: "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
  Abstract: In this paper, we consider the following graph partitioning problem: The input is an undirected graph $G=(V,E),$ a balance parameter $b \in (0,1/2]$ and a target conductance value $\gamma \in (0,1).$ The output is a cut which, if non-empty, is of conductance at most $O(f),$ for some function $f(G, \gamma),$ and which is either balanced or well correlated with all cuts of conductance at most $\gamma.$ Spielman and Teng gave an $\tilde{O}(|E|/\gamma^{2})$-time algorithm for $f= \sqrt{\gamma \log^{3}|V|}$ and used it to decompose graphs into a collection of near-expanders. We present a new spectral algorithm for this problem which runs in time $\tilde{O}(|E|/\gamma)$ for $f=\sqrt{\gamma}.$ Our result yields the first nearly-linear time algorithm for the classic Balanced Separator problem that achieves the asymptotically optimal approximation guarantee for spectral methods. Our method has the advantage of being conceptually simple and relies on a primal-dual semidefinite-programming SDP approach. We first consider a natural SDP relaxation for the Balanced Separator problem. While it is easy to obtain from this SDP a certificate of the fact that the graph has no balanced cut of conductance less than $\gamma,$ somewhat surprisingly, we can obtain a certificate for the stronger correlation condition. This is achieved via a novel separation oracle for our SDP and by appealing to Arora and Kale's framework to bound the running time. Our result contains technical ingredients that may be of independent interest.

Connected through hyperedge HE-1 (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-2 (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"
    - "expander decomposition and pruning faster stronger and simpler"
    - "a faster distributed single source shortest paths algorithm"
    - "distributed exact shortest paths in sublinear time"

Connected through hyperedge HE-3 (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"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"

Connected through hyperedge HE-4 (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"
    - "expander decomposition and pruning faster stronger and simpler"
    - "mst in o 1 rounds of the congested clique"
    - "fooling views a new lower bound technique for distributed computations under congestion"

Connected through hyperedge HE-5 (induced by source paper "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"):
  with members:
    - "non convex matrix completion against a semi random adversary"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "sampling random spanning trees faster than matrix multiplication"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "on sketching quadratic forms"
    - "algorithms for lipschitz learning on graphs"

Connected through hyperedge HE-6 (induced by source paper "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"):
  with members:
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "pseudorandomness and fourier growth bounds for width 3 branching programs"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"

Connected through hyperedge HE-7 (induced by source paper "on fully dynamic graph sparsifiers"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "algorithms for lipschitz learning on graphs"
    - "sketching cuts in graphs and hypergraphs"
    - "approximate undirected maximum flows in o m polylog n time"

Connected through hyperedge HE-8 (induced by source paper "heavy hitters via cluster preserving clustering"):
  with members:
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "beating countsketch for heavy hitters in insertion streams"
    - "linear time list recovery of high rate expander codes"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "flow based algorithms for local graph clustering"

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)