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: "the complexity of approximating vertex expansion"
  Abstract: We study the complexity of approximating the vertex expansion of graphs $G = (V,E)$, defined as \[ \Phi^V := \min_{S \subset V} n \cdot \frac{|N(S)|}{|S| |V \backslash S|}. \] #R##N#We give a simple polynomial-time algorithm for finding a subset with vertex expansion $O(\sqrt{OPT \log d})$ where $d$ is the maximum degree of the graph. Our main result is an asymptotically matching lower bound: under the Small Set Expansion (SSE) hypothesis, it is hard to find a subset with expansion less than $C\sqrt{OPT \log d}$ for an absolute constant $C$. In particular, this implies for all constant $\epsilon > 0$, it is SSE-hard to distinguish whether the vertex expansion $< \epsilon$ or at least an absolute constant. The analogous threshold for edge expansion is $\sqrt{OPT}$ with no dependence on the degree; thus our results suggest that vertex expansion is harder to approximate than edge expansion. In particular, while Cheeger's algorithm can certify constant edge expansion, it is SSE-hard to certify constant vertex expansion in graphs. #R##N#Our proof is via a reduction from the {\it Unique Games} instance obtained from the \SSE hypothesis to the vertex expansion problem. It involves the definition of a smoother intermediate problem we call {\sf Analytic Vertex Expansion} which is representative of both the vertex expansion and the conductance of the graph. Both reductions (from the UGC instance to this problem and from this problem to vertex expansion) use novel proof ideas.

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 "effect of gromov hyperbolicity parameter on cuts and expansions in graphs and some algorithmic implications"):
  with members:
    - "computing the gromov hyperbolicity of a discrete metric space"
    - "euclidean versus hyperbolic congestion in idealized versus experimental networks"
    - "reductions between expansion problems"

Connected through hyperedge HE-3 (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"
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "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-4 (induced by source paper "partitioning a graph into small pieces with applications to path transversal"):
  with members:
    - "linear kernels for edge deletion problems to immersion closed graph classes"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "hardness of approximation for h free edge modification problems"
    - "on the computational complexity of vertex integrity and component order connectivity"
    - "fixed parameter and approximation algorithms a new look"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"
    - "a new multilayered pcp and the hardness of hypergraph vertex cover"

Connected through hyperedge HE-5 (induced by source paper "the densest k subhypergraph problem"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set 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-6 (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"
    - "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-7 (induced by source paper "node expansions and cuts in gromov hyperbolic graphs"):
  with members:
    - "computing the gromov hyperbolicity of a discrete metric space"
    - "euclidean versus hyperbolic congestion in idealized versus experimental networks"

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"
    - "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.CC(Computational Complexity)