human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "information spreading in dynamic networks"
  Abstract: We study the fundamental problem of information spreading (also known as gossip) in dynamic networks. In gossip, or more generally, $k$-gossip, there are $k$ pieces of information (or tokens) that are initially present in some nodes and the problem is to disseminate the $k$ tokens to all nodes. The goal is to accomplish the task in as few rounds of distributed computation as possible. The problem is especially challenging in dynamic networks where the network topology can change from round to round and can be controlled by an on-line adversary. #R##N#The focus of this paper is on the power of token-forwarding algorithms, which do not manipulate tokens in any way other than storing and forwarding them. We first consider a worst-case adversarial model first studied by Kuhn, Lynch, and Oshman~\cite{kuhn+lo:dynamic} in which the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors for the current round are before they broadcast their messages. Our main result is an $\Omega(nk/\log n)$ lower bound on the number of rounds needed for any deterministic token-forwarding algorithm to solve $k$-gossip. This resolves an open problem raised in~\cite{kuhn+lo:dynamic}, improving their lower bound of $\Omega(n \log k)$, and matching their upper bound of $O(nk)$ to within a logarithmic factor. #R##N#We next show that token-forwarding algorithms can achieve subquadratic time in the offline version of the problem where the adversary has to commit all the topology changes in advance at the beginning of the computation, and present two polynomial-time offline token-forwarding algorithms. Our results are a step towards understanding the power and limitation of token-forwarding algorithms in dynamic networks.

Connected through hyperedge HE-1 (induced by source paper "global information sharing under network dynamics"):
  with members:
    - "information spreading in dynamic graphs"
    - "faster information dissemination in dynamic networks via network coding"
    - "order optimal information spreading using algebraic gossip"
    - "time varying graphs and dynamic networks"
    - "analyzing network coding gossip made easy"
    - "optimal gossip based aggregate computation"

Connected through hyperedge HE-2 (induced by source paper "lower bounds on information dissemination in dynamic networks"):
  with members:
    - "information spreading in dynamic graphs"
    - "one packet suffices highly efficient packetized network coding with finite memory"
    - "information spreading in stationary markovian evolving graphs"
    - "faster information dissemination in dynamic networks via network coding"
    - "optimal gradient clock synchronization in dynamic networks"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-3 (induced by source paper "fast distributed computation in dynamic networks via random walks"):
  with members:
    - "information spreading in dynamic graphs"
    - "faster information dissemination in dynamic networks via network coding"
    - "time varying graphs and dynamic networks"
    - "analyzing network coding gossip made easy"
    - "efficient distributed random walks with applications"

Question: Please tell me which class the center node 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)