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: "token curated registries a game theoretic approach"
  Abstract: Token curated registries (TCRs) have been proposed recently as an approach to create and maintain high quality lists of resources or recommendations in a decentralized manner. Applications range from maintaining registries of web domains for advertising purposes (e.g., adChain) or restaurants, consumer products, etc. The registry is maintained through a combination of candidate applications requiring a token deposit, challenges based on token staking and token-weighted votes with a redistribution of tokens occurring as a consequence of the vote. We present a simplified mathematical model of a TCR and its challenge and voting process analyze it from a game-theoretic perspective. We derive some insights into conditions with respect to the quality of a candidate under which challenges occur, and under which the outcome is reject or accept. We also show that there are conditions under which the outcome may not be entirely predictable in the sense that everyone voting for accept and everyone voting for reject could both be Nash Equilibria outcomes. For such conditions, we also explore when a particular strategy profile may be payoff dominant. We identify ways in which our modeling can be extended and also some implications of our model with respect to the composition of TCRs.

Connected through hyperedge HE-1 (induced by source paper "token curated registry with citation graph"):
  with members:
    - "enhancing engagement in token curated registries via an inflationary mechanism"
    - "a memo on the proof of stake mechanism"
    - "peer prediction with heterogeneous tasks"
    - "informed truthfulness in multi task peer prediction"
    - "augur a decentralized open source platform for prediction markets"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "recommendation on academic networks using direction aware citation analysis"

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)