human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "integrality gaps and approximation algorithms for dispersers and bipartite expanders"):
  Abstract: We study the problem of approximating the quality of a disperser. A bipartite graph $G$ on $([N],[M])$ is a $(\rho N,(1-\delta)M)$-disperser if for any subset $S\subseteq [N]$ of size $\rho N$, the neighbor set $\Gamma(S)$ contains at least $(1-\delta)M$ distinct vertices. Our main results are strong integrality gaps in the Lasserre hierarchy and an approximation algorithm for dispersers. #R##N#\begin{enumerate} #R##N#\item For any $\alpha>0$, $\delta>0$, and a random bipartite graph $G$ with left degree $D=O(\log N)$, we prove that the Lasserre hierarchy cannot distinguish whether $G$ is an $(N^{\alpha},(1-\delta)M)$-disperser or not an $(N^{1-\alpha},\delta M)$-disperser. #R##N#\item For any $\rho>0$, we prove that there exist infinitely many constants $d$ such that the Lasserre hierarchy cannot distinguish whether a random bipartite graph $G$ with right degree $d$ is a $(\rho N, (1-(1-\rho)^d)M)$-disperser or not a $(\rho N, (1-\Omega(\frac{1-\rho}{\rho d + 1-\rho}))M)$-disperser. We also provide an efficient algorithm to find a subset of size exact $\rho N$ that has an approximation ratio matching the integrality gap within an extra loss of $\frac{\min\{\frac{\rho}{1-\rho},\frac{1-\rho}{\rho}\}}{\log d}$. \end{enumerate} #R##N#Our method gives an integrality gap in the Lasserre hierarchy for bipartite expanders with left degree~$D$. $G$ on $([N],[M])$ is a $(\rho N,a)$-expander if for any subset $S\subseteq [N]$ of size $\rho N$, the neighbor set $\Gamma(S)$ contains at least $a \cdot \rho N$ distinct vertices. We prove that for any constant $\epsilon>0$, there exist constants $\epsilon'<\epsilon,\rho,$ and $D$ such that the Lasserre hierarchy cannot distinguish whether a bipartite graph on $([N],[M])$ with left degree $D$ is a $(\rho N, (1-\epsilon')D)$-expander or not a $(\rho N, (1-\epsilon)D)$-expander.

Includes members:
  Member "approximation algorithms for hypergraph small set expansion and small set vertex expansion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "the complexity of approximating vertex expansion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "constant factor lasserre integrality gaps for graph partitioning problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparsest cut on bounded treewidth graphs algorithms and hardness results")
  Member "approximation algorithm for non boolean max k csp":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximation of non boolean 2csp")
  Member "explicit optimal hardness via gaussian stability results":
    also connected through other hyperedges:
      (none)
  Member "better balance by being biased a 0 8776 approximation for max bisection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection")
  Member "min max graph partitioning and small set expansion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "polynomial integrality gaps for strong sdp relaxations of densest k subgraph":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "high dimensional estimation via sum of squares proofs")

Question: Please tell me which class the source paper 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)