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: "partitioning into expanders"
  Abstract: Let G=(V,E) be an undirected graph, lambda_k be the k-th smallest eigenvalue of the normalized laplacian matrix of G. There is a basic fact in algebraic graph theory that lambda_k > 0 if and only if G has at most k-1 connected components. We prove a robust version of this fact. If lambda_k>0, then for some 1\leq \ell\leq k-1, V can be {\em partitioned} into l sets P_1,\ldots,P_l such that each P_i is a low-conductance set in G and induces a high conductance induced subgraph. In particular, \phi(P_i)=O(l^3\sqrt{\lambda_l}) and \phi(G[P_i]) >= \lambda_k/k^2). #R##N#We make our results algorithmic by designing a simple polynomial time spectral algorithm to find such partitioning of G with a quadratic loss in the inside conductance of P_i's. Unlike the recent results on higher order Cheeger's inequality [LOT12,LRTV12], our algorithmic results do not use higher order eigenfunctions of G. If there is a sufficiently large gap between lambda_k and lambda_{k+1}, more precisely, if \lambda_{k+1} >= \poly(k) lambda_{k}^{1/4} then our algorithm finds a k partitioning of V into sets P_1,...,P_k such that the induced subgraph G[P_i] has a significantly larger conductance than the conductance of P_i in G. Such a partitioning may represent the best k clustering of G. Our algorithm is a simple local search that only uses the Spectral Partitioning algorithm as a subroutine. We expect to see further applications of this simple algorithm in clustering applications.

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"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "ferromagnetic potts model refined bis hardness and related results"
    - "approximating the expansion profile and almost optimal local graph clustering"

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 "improved analysis of spectral algorithm for clustering"):
  with members:
    - "convex programming based spectral clustering"
    - "a note on spectral clustering"
    - "approximate spectral clustering efficiency and guarantees"
    - "partitioning well clustered graphs spectral clustering works"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "convex programming based spectral clustering"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "spectral clustering by ellipsoid and its connection to separable nonnegative matrix factorization"
    - "a note on spectral clustering"
    - "partitioning well clustered graphs spectral clustering works"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "defining and evaluating network communities based on ground truth"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-5 (induced by source paper "testing graph clusterability algorithms and lower bounds"):
  with members:
    - "testing bounded arboricity"
    - "on approximating the number of k cliques in sublinear time"
    - "estimating graph parameters from random order streams"
    - "on sampling edges almost uniformly"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "testing cluster structure of graphs"
    - "approximately counting triangles in sublinear time"
    - "a simpler sublinear algorithm for approximating the triangle count"

Connected through hyperedge HE-6 (induced by source paper "simulated tempering langevin monte carlo ii an improved proof using soft markov chain decomposition"):
  with members:
    - "rapid convergence of the unadjusted langevin algorithm log sobolev suffices"
    - "beyond log concavity provable guarantees for sampling multi modal distributions using simulated tempering langevin monte carlo"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"

Connected through hyperedge HE-7 (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"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

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)