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: "dynamic assessments matching and allocation of tasks"
  Abstract: In many two-sided markets, each side has incomplete information about the other but has an opportunity to learn (some) relevant information before final matches are made. For instance, clients seeking workers to perform tasks often conduct interviews that require the workers to perform some tasks and thereby provide information to both sides. The performance of a worker in such an interview/assessment - and hence the information revealed - depends both on the inherent characteristics of the worker and the task and also on the actions taken by the worker (e.g. the effort expended); thus there is both adverse selection (on both sides) and moral hazard (on one side). When interactions are ongoing, incentives for workers to expend effort in the current assessment can be provided by the payment rule used and also by the matching rule that assesses and determines the tasks to which the worker is assigned in the future; thus workers have career concerns. We derive mechanisms - payment, assessment and matching rules - that lead to final matchings that are stable in the long run and achieve close to the optimal performance (profit or social welfare maximizing) in equilibrium (unique) thus mitigating both adverse selection and moral hazard (in many settings).

Connected through hyperedge HE-1 (induced by source paper "context aware hierarchical online learning for performance maximization in mobile crowdsourcing"):
  with members:
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "distributed online learning in social recommender systems"
    - "online clustering of bandits"
    - "resourceful contextual bandits"
    - "distributed online learning via cooperative contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "fast reinforcement learning for energy efficient wireless communication"
    - "a contextual bandit approach to personalized news article recommendation"

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)