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: "gossip based information spreading in mobile networks"
  Abstract: Mobile networks receive increasing research interest recently due to their increasingly wide applications in various areas; mobile ad hoc networks (MANET) and Vehicular ad hoc networks (VANET) are two prominent examples. Mobility introduces challenges as well as opportunities: it is known to improve the network throughput as shown in [1]. In this paper, we analyze the effect of mobility on the information spreading based on gossip algorithms. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we explore the mobile conductances of several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.

Connected through hyperedge HE-1 (induced by source paper "virtual mimo boosted information propagation on highways"):
  with members:
    - "mobile conductance in sparse networks and mobility connectivity tradeoff"
    - "faster information propagation on highways a virtual mimo approach"

Connected through hyperedge HE-2 (induced by source paper "optimal forwarding in opportunistic delay tolerant networks with meeting rate estimations"):
  with members:
    - "the heterogeneity of inter contact time distributions its importance for routing in delay tolerant networks"

Connected through hyperedge HE-3 (induced by source paper "mobile conductance in sparse networks and mobility connectivity tradeoff"):
  with members:
    - "mobile conductance and gossip based information spreading in mobile networks"
    - "tight bounds on information dissemination in sparse mobile networks"
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "manets high mobility can make up for low transmission power"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-4 (induced by source paper "location aided energy balancing strategy in green cellular networks"):
  with members:
    - "mobile conductance in sparse networks and mobility connectivity tradeoff"
    - "adaptive learning of region based plsa model for total scene annotation"
    - "green cellular networks a survey some research issues and challenges"

Connected through hyperedge HE-5 (induced by source paper "mobile conductance and gossip based information spreading in mobile networks"):
  with members:
    - "information spreading in stationary markovian evolving graphs"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-6 (induced by source paper "rateless coding assisted multi packet spreading over mobile networks"):
  with members:
    - "mobile conductance and gossip based information spreading in mobile networks"
    - "information spreading in stationary markovian evolving graphs"

Connected through hyperedge HE-7 (induced by source paper "exploiting delay correlation for multi antenna assisted high speed train communications"):
  with members:
    - "interference aware resource control in multi antenna cognitive ad hoc networks with heterogeneous delay constraints"

Connected through hyperedge HE-8 (induced by source paper "faster information propagation on highways a virtual mimo approach"):
  with members:
    - "mobile conductance in sparse networks and mobility connectivity tradeoff"

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.SI(Social and Information Networks)