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 "beyond alice and bob improved inapproximability for maximum independent set in congest"):
  Abstract: By far the most fruitful technique for showing lower bounds for the CONGEST model is reductions to two-party communication complexity. This technique has yielded nearly tight results for various fundamental problems such as distance computations, minimum spanning tree, minimum vertex cover, and more. #R##N#In this work, we take this technique a step further, and we introduce a framework of reductions to $t$-party communication complexity, for every $t\geq 2$. Our framework enables us to show improved hardness results for maximum independent set. Recently, Bachrach et al.[PODC 2019] used the two-party framework to show hardness of approximation for maximum independent set. They show that finding a $(5/6+\epsilon)$-approximation requires $\Omega(n/\log^6 n)$ rounds, and finding a $(7/8+\epsilon)$-approximation requires $\Omega(n^2/\log^7 n)$ rounds, in the CONGEST model where $n$ in the number of nodes in the network. #R##N#We improve the results of Bachrach et al. by using reductions to multi-party communication complexity. Our results: #R##N#(1) Any algorithm that finds a $(1/2+\epsilon)$-approximation for maximum independent set in the CONGEST model requires $\Omega(n/\log^3 n)$ rounds. #R##N#(2) Any algorithm that finds a $(3/4+\epsilon)$-approximation for maximum independent set in the CONGEST model requires $\Omega(n^2/\log^3 n)$ rounds.

Includes members:
  Member "improved distributed approximation to maximum independent set":
    also connected through other hyperedges:
      (none)
  Member "detecting cliques in congest networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed testing of graph isomorphism in the congest model")
  Member "distributed spanner approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "fooling views a new lower bound technique for distributed computations under congestion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed testing of graph isomorphism in the congest model")
  Member "quadratic and near quadratic lower bounds for the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "distributed approximation of maximum independent set and maximum matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "near linear lower bounds for distributed distance computations even in sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "distributed construction of purely additive spanners":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph spanners a tutorial review")

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.DC(Distributed, Parallel, and Cluster Computing)