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: "min max graph partitioning and small set expansion"
  Abstract: We study graph partitioning problems from a min-max perspective, in which an input graph on n vertices should be partitioned into k parts, and the objective is to minimize the maximum number of edges leaving a single part. The two main versions we consider are where the k parts need to be of equal-size, and where they must separate a set of k given terminals. We consider a common generalization of these two problems, and design for it an $O(\sqrt{\log n\log k})$-approximation algorithm. This improves over an $O(\log^2 n)$ approximation for the second version, and roughly $O(k\log n)$ approximation for the first version that follows from other previous work. We also give an improved O(1)-approximation algorithm for graphs that exclude any fixed minor. #R##N#Our algorithm uses a new procedure for solving the Small-Set Expansion problem. In this problem, we are given a graph G and the goal is to find a non-empty set $S\subseteq V$ of size $|S| \leq \rho n$ with minimum edge-expansion. We give an $O(\sqrt{\log{n}\log{(1/\rho)}})$ bicriteria approximation algorithm for the general case of Small-Set Expansion, and O(1) approximation algorithm for graphs that exclude any fixed minor.

Connected through hyperedge HE-1 (induced by source paper "planted models for k way edge and vertex expansion"):
  with members:
    - "semi random graphs with planted sparse vertex cuts algorithms for exact and approximate recovery"
    - "multisection in the stochastic block model using semidefinite programming"
    - "learning communities in the presence of errors"
    - "how robust are reconstruction thresholds for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "constant factor approximation for balanced cut in the pie model"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-2 (induced by source paper "approximating unique games using low diameter graph decomposition"):
  with members:
    - "unique games on the hypercube"
    - "spectral algorithms for unique games"
    - "reductions between expansion problems"

Connected through hyperedge HE-3 (induced by source paper "graph clustering using effective resistance"):
  with members:
    - "a nearly mlogn time solver for sdd linear systems"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-4 (induced by source paper "spectral sparsification and regret minimization beyond matrix multiplicative updates"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "an efficient algorithm for unweighted spectral graph sparsification"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"
    - "near optimal algorithms for online matrix prediction"
    - "a matrix hyperbolic cosine algorithm and applications"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "integrality gaps and approximation algorithms for dispersers and bipartite expanders"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"
    - "constant factor lasserre integrality gaps for graph partitioning problems"
    - "approximation algorithm for non boolean max k csp"
    - "explicit optimal hardness via gaussian stability results"
    - "better balance by being biased a 0 8776 approximation for max bisection"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"
    - "approximating csps with global cardinality constraints using sdp hierarchies"

Connected through hyperedge HE-6 (induced by source paper "how to round subspaces a new spectral clustering algorithm"):
  with members:
    - "spectral clustering via the power method provably"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "improved spectral norm bounds for clustering"
    - "many sparse cuts via higher eigenvalues"
    - "optimal column based low rank matrix reconstruction"
    - "clustering with spectral norm and the k means algorithm"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-7 (induced by source paper "approximation algorithms for finding maximum induced expanders"):
  with members:
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "the kadison singer problem for strongly rayleigh measures and applications to asymmetric tsp"
    - "partitioning into expanders"
    - "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"
    - "finding sparse cuts locally using evolving sets"

Connected through hyperedge HE-8 (induced by source paper "hypergraph markov operators eigenvalues and approximation algorithms"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "the complexity of approximating vertex expansion"
    - "approximation algorithm for sparsest k partitioning"
    - "many sparse cuts via higher eigenvalues"
    - "most tensor problems are np hard"
    - "random tensors and planted cliques"

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)