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: "greedy gossip with eavesdropping"
  Abstract: This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper, we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.

Connected through hyperedge HE-1 (induced by source paper "sample greedy gossip for distributed network wide average computation"):
  with members:
    - "randomized consensus based distributed kalman filtering over wireless sensor networks"
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "gossip and distributed kalman filtering weak consensus under weak detectability"

Connected through hyperedge HE-2 (induced by source paper "sample greedy gossip distributed kalman filter"):
  with members:
    - "analysis of sum weight like algorithms for averaging in wireless sensor networks"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-3 (induced by source paper "epidemic like proximity based traffic offloading"):
  with members:
    - "mechanism design for base station association and resource allocation in downlink ofdma network"
    - "non cooperative game for capacity offload"
    - "the impact of mobility on gossip algorithms"
    - "femtocell networks a survey"

Connected through hyperedge HE-4 (induced by source paper "analysis of sum weight like algorithms for averaging in wireless sensor networks"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "geographic gossip efficient averaging for sensor networks"
    - "order optimal consensus through randomized path averaging"

Connected through hyperedge HE-5 (induced by source paper "the impact of mobility on gossip algorithms"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "geographic gossip efficient averaging for sensor networks"
    - "order optimal consensus through randomized path averaging"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"
    - "gossiping with multiple messages"

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)