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: "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
  Abstract: The expansion of a hypergraph, a natural extension of the notion of expansion in graphs, is defined as the minimum over all cuts in the hypergraph of the ratio of the number of the hyperedges cut to the size of the smaller side of the cut. We study the Hypergraph Small Set Expansion problem, which, for a parameter $\delta \in (0,1/2]$, asks to compute the cut having the least expansion while having at most $\delta$ fraction of the vertices on the smaller side of the cut. We present two algorithms. Our first algorithm gives an $\tilde O(\delta^{-1} \sqrt{\log n})$ approximation. The second algorithm finds a set with expansion $\tilde O(\delta^{-1}(\sqrt{d_{\text{max}}r^{-1}\log r\, \phi^*} + \phi^*))$ in a $r$--uniform hypergraph with maximum degree $d_{\text{max}}$ (where $\phi^*$ is the expansion of the optimal solution). Using these results, we also obtain algorithms for the Small Set Vertex Expansion problem: we get an $\tilde O(\delta^{-1} \sqrt{\log n})$ approximation algorithm and an algorithm that finds a set with vertex expansion $O\left(\delta^{-1}\sqrt{\phi^V \log d_{\text{max}} } + \delta^{-1} \phi^V\right)$ (where $\phi^V$ is the vertex expansion of the optimal solution). #R##N#For $\delta=1/2$, Hypergraph Small Set Expansion is equivalent to the hypergraph expansion problem. In this case, our approximation factor of $O(\sqrt{\log n})$ for expansion in hypergraphs matches the corresponding approximation factor for expansion in graphs due to ARV.

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"
    - "exact recovery in the stochastic block model"
    - "the complexity of approximating vertex expansion"

Connected through hyperedge HE-2 (induced by source paper "spectral properties of hypergraph laplacian and approximation algorithms"):
  with members:
    - "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "the complexity of approximating vertex expansion"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "reductions between expansion problems"
    - "most tensor problems are np hard"

Connected through hyperedge HE-3 (induced by source paper "the densest k subhypergraph problem"):
  with members:
    - "the complexity of approximating vertex expansion"
    - "approximating semi matchings in streaming and in two party communication"
    - "everywhere sparse spanners via dense subgraphs"
    - "approximation algorithms and hardness of the k route cut problem"
    - "small approximate pareto sets for bi objective shortest paths and other problems"
    - "dial a ride from k forest"

Connected through hyperedge HE-4 (induced by source paper "minimizing the union tight approximations for small set bipartite vertex expansion"):
  with members:
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"
    - "everywhere sparse spanners via dense subgraphs"

Connected through hyperedge HE-5 (induced by source paper "integrality gaps and approximation algorithms for dispersers and bipartite expanders"):
  with members:
    - "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"
    - "min max graph partitioning and small set expansion"
    - "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 "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs"):
  with members:
    - "hypergraph markov operators eigenvalues and approximation algorithms"

Connected through hyperedge HE-7 (induced by source paper "sketching cuts in graphs and hypergraphs"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "the sketching complexity of graph cuts"
    - "streaming lower bounds for approximating max cut"
    - "single pass spectral sparsification in dynamic streams"
    - "single pass sparsification in the streaming model with edge deletions"
    - "graph sparsification in the semi streaming model"
    - "randomized approximation schemes for cuts and flows in capacitated graphs"

Connected through hyperedge HE-8 (induced by source paper "hypergraph markov operators eigenvalues and approximation algorithms"):
  with members:
    - "the complexity of approximating vertex expansion"
    - "approximation algorithm for sparsest k partitioning"
    - "many sparse cuts via higher eigenvalues"
    - "min max graph partitioning and small set expansion"
    - "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)