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: "approximating the expansion profile and almost optimal local graph clustering"
  Abstract: Spectral partitioning is a simple, nearly-linear time, algorithm to find sparse cuts, and the Cheeger inequalities provide a worst-case guarantee for the quality of the approximation found by the algorithm. Local graph partitioning algorithms [ST08,ACL06,AP09] run in time that is nearly linear in the size of the output set, and their approximation guarantee is worse than the guarantee provided by the Cheeger inequalities by a polylogarithmic $\log^{\Omega(1)} n$ factor. It has been a long standing open problem to design a local graph clustering algorithm with an approximation guarantee close to the guarantee of the Cheeger inequalities and with a running time nearly linear in the size of the output. #R##N#In this paper we solve this problem; we design an algorithm with the same guarantee (up to a constant factor) as the Cheeger inequality, that runs in time slightly super linear in the size of the output. This is the first sublinear (in the size of the input) time algorithm with almost the same guarantee as the Cheeger's inequality. As a byproduct of our results, we prove a bicriteria approximation algorithm for the expansion profile of any graph. Let $\phi(\gamma) = \min_{\mu(S) \leq \gamma}\phi(S)$. There is a polynomial time algorithm that, for any $\gamma,\epsilon>0$, finds a set $S$ of measure $\mu(S)\leq 2\gamma^{1+\epsilon}$, and expansion $\phi(S)\leq \sqrt{2\phi(\gamma)/\epsilon}$. Our proof techniques also provide a simpler proof of the structural result of Arora, Barak, Steurer [ABS10], that can be applied to irregular graphs. #R##N#Our main technical tool is that for any set $S$ of vertices of a graph, a lazy $t$-step random walk started from a randomly chosen vertex of $S$, will remain entirely inside $S$ with probability at least $(1-\phi(S)/2)^t$. This itself provides a new lower bound to the uniform mixing time of any finite states reversible markov chain.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for the potts model on small set expanders"):
  with members:
    - "counting independent sets in unbalanced bipartite graphs"
    - "fast algorithms at low temperatures via markov chains"
    - "statistical physics approaches to unique games"
    - "algorithms for bis hard problems on expander graphs"
    - "algorithmic pirogov sinai theory"
    - "partitioning into expanders"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "ferromagnetic potts model refined bis hardness and related results"

Connected through hyperedge HE-2 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs"):
  with members:
    - "testing graph clusterability algorithms and lower bounds"
    - "approximate correlation clustering using same cluster queries"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "learning communities in the presence of errors"
    - "testing cluster structure of graphs"
    - "how robust are reconstruction thresholds for community detection"
    - "correlation clustering with noisy partial information"

Connected through hyperedge HE-3 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "parallel local graph clustering"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"

Connected through hyperedge HE-4 (induced by source paper "breaking quadratic time for small vertex connectivity and an approximation scheme"):
  with members:
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "expander decomposition and pruning faster stronger and simpler"
    - "capacity releasing diffusion for speed and locality"
    - "local flow partitioning for faster edge connectivity"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "a simple and strongly local flow based method for cut improvement"
    - "fully dynamic minimum spanning forest with improved worst case update time"
    - "deterministic global minimum cut of a simple graph in near linear time"

Connected through hyperedge HE-5 (induced by source paper "distributed graph clustering and sparsification"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "communication optimal distributed clustering"
    - "average whenever you meet opportunistic protocols for community detection"
    - "find your place simple distributed algorithms for community detection"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-6 (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-7 (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"

Connected through hyperedge HE-8 (induced by source paper "parallel local graph clustering"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "defining and evaluating network communities based on ground truth"

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)