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 "the communication cost of information spreading in dynamic networks"):
  Abstract: This paper investigates the message complexity of distributed information spreading (a.k.a gossip or token dissemination) in adversarial dynamic networks, where the goal is to spread $k$ tokens of information to every node on an $n$-node network. We consider the amortized (average) message complexity of spreading a token, assuming that the number of tokens is large. Our focus is on token-forwarding algorithms, which do not manipulate tokens in any way other than storing, copying, and forwarding them. #R##N#We consider two types of adversaries that arbitrarily rewire the network while keeping it connected: the adaptive adversary that is aware of the status of all the nodes and the algorithm (including the current random choices), and the oblivious adversary that is oblivious to the random choices made by the algorithm. The central question that motivates our work is whether one can achieve subquadratic amortized message complexity for information spreading. #R##N#We present two sets of results depending on how nodes send messages to their neighbors: (1) Local broadcast: We show a tight lower bound of $\Omega(n^2)$ on the number of amortized local broadcasts, which is matched by the naive flooding algorithm, (2) Unicast: We study the message complexity as a function of the number of dynamic changes in the network. To facilitate this, we introduce a natural complexity measure for analyzing dynamic networks called adversary-competitive message complexity where the adversary pays a unit cost for every topological change. Under this model, it is shown that if $k$ is sufficiently large, we can obtain an optimal amortized message complexity of $O(n)$. We also present a randomized algorithm that achieves subquadratic amortized message complexity when the number of tokens is not large under an oblivious adversary.

Includes members:
  Member "information spreading in dynamic networks under oblivious adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "local distributed algorithms in highly dynamic networks")
  Member "construction and impromptu repair of an mst in a distributed network with o m communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "message reduction in the local model is a free lunch")
  Member "rumor spreading in random evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the radius of nonsplit graphs and information dissemination in dynamic networks")
  Member "distributed random walks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "walking randomly massively and efficiently")
  Member "lower bounds on information dissemination in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gathering in dynamic rings")
  Member "parsimonious flooding in geometric random walks":
    also connected through other hyperedges:
      (none)
  Member "faster information dissemination in dynamic networks via network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local distributed algorithms in highly dynamic networks")
  Member "time varying graphs and dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edge disjoint branchings in temporal graphs")

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)