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: "sharing in networks of strategic agents"
  Abstract: In social, economic and engineering networks, connected agents need to cooperate by repeatedly sharing information and/or goods. Typically, sharing is costly and there are no immediate benefits for agents who share. Hence, agents who strategically aim to maximize their own individual utilities will “free-ride” because they lack incentives to cooperate/share, thereby leading to inefficient operation or even collapse of networks. To incentivize the strategic agents to cooperate with each other, we design distributed rating protocols which exploit the ongoing nature of the agents' interactions to assign ratings and through them, determine future rewards and punishments: agents that have behaved as directed enjoy high ratings-and hence greater future access to the information/goods of others; agents that have not behaved as directed enjoy low ratings-and hence less future access to the information/goods of others. Unlike existing rating protocols, the proposed protocol operates in a distributed manner and takes into consideration the underlying interconnectivity of agents as well as their heterogeneity. We prove that in many networks, the price of anarchy (PoA) obtained by adopting the proposed rating protocols is 1, that is, the optimal social welfare is attained. In networks where PoA is larger than 1, we show that the proposed rating protocol significantly outperforms existing incentive mechanisms. Last but not least, the proposed rating protocols can also operate efficiently in dynamic networks, where new agents enter the network over time.

Connected through hyperedge HE-1 (induced by source paper "information sharing over adaptive networks with self interested agents"):
  with members:
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "reputation based incentive protocols in crowdsourcing 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.GT(Computer Science and Game Theory)