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 "connectivity lower bounds in broadcast congested clique"):
  Abstract: We prove three new lower bounds for graph connectivity in the $1$-bit broadcast congested clique model, BCC$(1)$. First, in the KT-$0$ version of BCC$(1)$, in which nodes are aware of neighbors only through port numbers, we show an $\Omega(\log n)$ round lower bound for CONNECTIVITY even for constant-error randomized Monte Carlo algorithms. The deterministic version of this result can be obtained via the well-known "edge-crossing" argument, but, the randomized version of this result requires establishing new combinatorial results regarding the indistinguishability graph induced by inputs. In our second result, we show that the $\Omega(\log n)$ lower bound result extends to the KT-$1$ version of the BCC$(1)$ model, in which nodes are aware of IDs of all neighbors, though our proof works only for deterministic algorithms. Since nodes know IDs of their neighbors in the KT-$1$ model, it is no longer possible to play "edge-crossing" tricks; instead we present a reduction from the 2-party communication complexity problem PARTITION in which Alice and Bob are give two set partitions on $[n]$ and are required to determine if the join of these two set partitions equals the trivial one-part set partition. While our KT-$1$ CONNECTIVITY lower bound holds only for deterministic algorithms, in our third result we extend this $\Omega(\log n)$ KT-1 lower bound to constant-error Monte Carlo algorithms for the closely related CONNECTED COMPONENTS problem. We use information-theoretic techniques to obtain this result. All our results hold for the seemingly easy special case of CONNECTIVITY in which an algorithm has to distinguish an instance with one cycle from an instance with multiple cycles. Our results showcase three rather different lower bound techniques and lay the groundwork for further improvements in lower bounds for CONNECTIVITY in the BCC$(1)$ model.

Includes members:
  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 "mst in o 1 rounds of the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing")
  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 "proof labeling schemes broadcast unicast and in between":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "introduction to local certification")
  Member "quadratic and near quadratic lower bounds for the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "deterministic graph connectivity in the broadcast congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "broadcast congested clique planted cliques and pseudorandom generators")
  Member "approximation of distances and shortest paths in the broadcast congest clique":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "distributed verification and hardness of distributed approximation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")

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)