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 "broadcast congested clique planted cliques and pseudorandom generators"):
  Abstract: We develop techniques to prove lower bounds for the BCAST(log n) Broadcast Congested Clique model (a distributed message passing model where in each round, each processor can broadcast an O(log n)-sized message to all other processors). Our techniques are built to prove bounds for natural input distributions. So far, all lower bounds for problems in the model relied on constructing specifically tailored graph families for the specific problem at hand, resulting in lower bounds for artificially constructed inputs, instead of natural input distributions. #R##N#One of our results is a lower bound for the directed planted clique problem. In this problem, an input graph is either a random directed graph (each directed edge is included with probability 1/2), or a random graph with a planted clique of size k. That is, k randomly chosen vertices have all of the edges between them included, and all other edges in the graph appear with probability 1/2. The goal is to determine whether a clique exists. We show that when k = n^(1/4 - eps), this problem requires a number of rounds polynomial in n. #R##N#Additionally, we construct a pseudo-random generator which fools the Broadcast Congested Clique. This allows us to show that every k round randomized algorithm in which each processor uses up to n random bits can be efficiently transformed into an O(k)-round randomized algorithm in which each processor uses only up to O(k log n) random bits, while maintaining a high success probability. The pseudo-random generator is simple to describe, computationally very cheap, and its seed size is optimal up to constant factors. However, the analysis is quite involved, and is based on the new technique for proving lower bounds in the model. #R##N#The technique also allows us to prove the first average case lower bound for the Broadcast Congested Clique, as well as an average-case time hierarchy.

Includes members:
  Member "congested clique algorithms for graph spanners":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "massively parallel algorithms for distance approximation and spanners")
  Member "optimal lower bounds for distributed and streaming spanning forest computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "msf and connectivity in limited variants of the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithms for noisy broadcast under erasures")
  Member "a time hierarchy theorem for the local model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed graph problems through an automata theoretic lens")
  Member "on derandomizing local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "deterministic subgraph detection in broadcast congest":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed testing of graph isomorphism in the congest model")
  Member "towards a complexity theory for the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "deterministic graph connectivity in the broadcast congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "connectivity lower bounds in broadcast congested clique")

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)