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: "social status and badge design"
  Abstract: Many websites rely on user-generated content to provide value to consumers. These websites typically incentivize participation by awarding users badges based on their contributions. While these badges typically have no explicit value, they act as symbols of social status within a community. In this paper, we consider the design of badge mechanisms for the objective of maximizing the total contributions made to a website. Users exert costly effort to make contributions and, in return, are awarded with badges. A badge is only valued to the extent that it signals social status and thus badge valuations are determined endogenously by the number of users who earn each badge. The goal of this paper is to study the design of optimal and approximately badge mechanisms under these status valuations. We characterize badge mechanisms by whether they use a coarse partitioning scheme, i.e. awarding the same badge to many users, or use a fine partitioning scheme, i.e. awarding a unique badge to most users. We find that the optimal mechanism uses both fine partitioning and coarse partitioning. When status valuations exhibit a decreasing marginal value property, we prove that coarse partitioning is a necessary feature of any approximately optimal mechanism. Conversely, when status valuations exhibit an increasing marginal value property, we prove that fine partitioning is necessary for approximate optimality.

Connected through hyperedge HE-1 (induced by source paper "activity archetypes in question and answer q a websites a study of 50 stack exchange instances"):
  with members:
    - "distributed and parallel time series feature extraction for industrial big data applications"
    - "activity dynamics in collaboration networks"
    - "chaff from the wheat characterization and modeling of deleted questions on stack overflow"

Connected through hyperedge HE-2 (induced by source paper "combinatorial bandits for incentivizing agents with dynamic preferences"):
  with members:
    - "a multi armed bandit approach for online expert selection in markov decision processes"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "bandits with knapsacks"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "badge system analysis and design"):
  with members:
    - "implementing optimal outcomes in social computing a game theoretic approach"

Connected through hyperedge HE-4 (induced by source paper "bayesian budget feasibility with posted pricing"):
  with members:
    - "incentivizing high quality crowdwork"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "budget feasible mechanism design from prior free to bayesian"
    - "prior free auctions for budgeted agents"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "optimal crowdsourcing contests"
    - "mechanism design via correlation gap"
    - "on the approximability of budget feasible mechanisms"

Connected through hyperedge HE-5 (induced by source paper "behavioral mechanism design optimal contests for simple agents"):
  with members:
    - "crowdsourcing with endogenous entry"
    - "optimal crowdsourcing contests"
    - "the labor economics of paid crowdsourcing"

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)