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 "detecting communities is hard and counting them is even harder"):
  Abstract: We consider the algorithmic problem of community detection in networks. Given an undirected friendship graph $G=\left(V,E\right)$, a subset $S\subseteq V$ is an $\left(\alpha,\beta\right)$-community if: #R##N#* Every member of the community is friends with an $\alpha$-fraction of the community; #R##N#* Every non-member is friends with at most a $\beta$-fraction of the community. #R##N#Arora et al [AGSS12] gave a quasi-polynomial time algorithm for enumerating all the $\left(\alpha,\beta\right)$-communities for any constants $\alpha>\beta$. #R##N#Here, we prove that, assuming the Exponential Time Hypothesis (ETH), quasi-polynomial time is in fact necessary - and even for a much weaker approximation desideratum. Namely, distinguishing between: #R##N#* $G$ contains an $\left(1,o\left(1\right)\right)$-community; and #R##N#* $G$ does not contain an $\left(\beta+o\left(1\right),\beta\right)$-community for any $\beta\in\left[0,1\right]$. #R##N#We also prove that counting the number of $\left(1,o\left(1\right)\right)$-communities requires quasi-polynomial time assuming the weaker #ETH.

Includes members:
  Member "community recovery in graphs with locality":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "settling the complexity of computing approximate two player nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "how robust are reconstruction thresholds for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improper learning for non stochastic control")
  Member "eth hardness for signaling in symmetric zero sum games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inapproximability of vc dimension and littlestone s dimension")
  Member "hardness results for signaling in bayesian zero sum and network routing games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive")
  Member "mixture selection mechanism design and signaling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the complexity of contracts")
  Member "am with multiple merlins":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "axioms for graph clustering quality functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graph clustering via qubo and digital annealing")

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)