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: "learning cooperative games"
  Abstract: This paper explores a PAC (probably approximately correct) learning model in cooperative games. Specifically, we are given $m$ random samples of coalitions and their values, taken from some unknown cooperative game; can we predict the values of unseen coalitions? We study the PAC learnability of several well-known classes of cooperative games, such as network flow games, threshold task games, and induced subgraph games. We also establish a novel connection between PAC learnability and core stability: for games that are efficiently learnable, it is possible to find payoff divisions that are likely to be stable using a polynomial number of samples.

Connected through hyperedge HE-1 (induced by source paper "a learning framework for distribution based game theoretic solution concepts"):
  with members:
    - "forming probably stable communities with limited interactions"
    - "envy free classification"
    - "statistical cost sharing"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "hedonic games with graph restricted communication"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-2 (induced by source paper "robustness against agent failure in hedonic games"):
  with members:
    - "group activity selection on social networks"
    - "hedonic games with graph restricted communication"
    - "group activity selection problem"
    - "solving cooperative reliability games"

Connected through hyperedge HE-3 (induced by source paper "overlapping coalition formation via probabilistic topic modeling"):
  with members:
    - "cooperative games with overlapping coalitions"

Connected through hyperedge HE-4 (induced by source paper "statistical cost sharing"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the limitations of optimization from samples"
    - "sampling and representation complexity of revenue maximization"
    - "mechanism design for data science"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "intriguing properties of neural networks"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning coverage functions and private release of marginals"

Connected through hyperedge HE-5 (induced by source paper "a characterization of monotone influence measures for data classification"):
  with members:
    - "axiomatic attribution for deep networks"
    - "use privacy in data driven systems theory and experiments with machine learnt programs"
    - "learning important features through propagating activation differences"
    - "statistical cost sharing"
    - "challenges for transparency"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "proxy non discrimination in data driven systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "learning adversary behavior in security games a pac model perspective"):
  with members:
    - "audit games with multiple defender resources"

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)