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: "discovery through gossip"
  Abstract: We study randomized gossip-based processes in dynamic networks that are motivated by discovery processes in large-scale distributed networks like peer-to-peer or social networks. #R##N#A well-studied problem in peer-to-peer networks is the resource discovery problem. There, the goal for nodes (hosts with IP addresses) is to discover the IP addresses of all other hosts. In social networks, nodes (people) discover new nodes through exchanging contacts with their neighbors (friends). In both cases the discovery of new nodes changes the underlying network - new edges are added to the network - and the process continues in the changed network. Rigorously analyzing such dynamic (stochastic) processes with a continuously self-changing topology remains a challenging problem with obvious applications. #R##N#This paper studies and analyzes two natural gossip-based discovery processes. In the push process, each node repeatedly chooses two random neighbors and puts them in contact (i.e., "pushes" their mutual information to each other). In the pull discovery process, each node repeatedly requests or "pulls" a random contact from a random neighbor. Both processes are lightweight, local, and naturally robust due to their randomization. #R##N#Our main result is an almost-tight analysis of the time taken for these two randomized processes to converge. We show that in any undirected n-node graph both processes take O(n log^2 n) rounds to connect every node to all other nodes with high probability, whereas Omega(n log n) is a lower bound. In the directed case we give an O(n^2 log n) upper bound and an Omega(n^2) lower bound for strongly connected directed graphs. A key technical challenge that we overcome is the analysis of a randomized process that itself results in a constantly changing network which leads to complicated dependencies in every round.

Connected through hyperedge HE-1 (induced by source paper "strengthening gossip protocols using protocol dependent knowledge"):
  with members:
    - "dynamic gossip"
    - "a logic of blockchain updates"
    - "common knowledge in a logic of gossips"
    - "epistemic protocols for distributed gossiping"
    - "plurality voting under uncertainty"
    - "simple fast and deterministic gossip and rumor spreading"
    - "dominating manipulations in voting with partial information"

Connected through hyperedge HE-2 (induced by source paper "scalable byzantine reliable broadcast extended version"):
  with members:
    - "the consensus number of a cryptocurrency"
    - "order optimal information spreading using algebraic gossip"

Connected through hyperedge HE-3 (induced by source paper "dynamic gossip"):
  with members:
    - "simple fast and deterministic gossip and rumor spreading"
    - "quasirandom rumor spreading"

Connected through hyperedge HE-4 (induced by source paper "highly intensive data dissemination in complex networks"):
  with members:
    - "new trends in parallel and distributed simulation from many cores to cloud computing"
    - "searching in unstructured overlays using local knowledge and gossip"
    - "publish subscribe systems via gossip a study based on complex networks"
    - "lunes agent based simulation of p2p systems"
    - "adaptive event dissemination for peer to peer multiplayer online games"

Connected through hyperedge HE-5 (induced by source paper "tight bounds for distributed graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "near optimal clustering in the k machine model"
    - "worst case optimal algorithms for parallel query processing"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "the input output complexity of triangle enumeration"

Connected through hyperedge HE-6 (induced by source paper "self stabilizing repeated balls into bins"):
  with members:
    - "plurality consensus in the gossip model"
    - "on the influence of graph density on randomized gossiping"
    - "tight thresholds for cuckoo hashing via xorsat"

Connected through hyperedge HE-7 (induced by source paper "plurality consensus in the gossip model"):
  with members:
    - "the power of two choices in distributed voting"
    - "determining majority in networks with local interactions and very small local memory"
    - "rumor spreading in random evolving graphs"
    - "distributed community detection in dynamic graphs"
    - "simple dynamics for plurality consensus"
    - "timing in chemical reaction networks"
    - "consensus on the initial global majority by local majority polling for a class of sparse graphs"
    - "near optimal random walk sampling in distributed networks"

Connected through hyperedge HE-8 (induced by source paper "optimal gossip with direct addressing"):
  with members:
    - "simple fast and deterministic gossip and rumor spreading"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "analyzing network coding gossip made easy"

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)