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: "gib imperfect information in a computationally challenging game"
  Abstract: This paper investigates the problems arising in the construction of a program to play the game of contract bridge. These problems include both the difficulty of solving the game's perfect information variant, and techniques needed to address the fact that bridge is not, in fact, a perfect information game. GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems.#R##N##R##N#GIB is currently believed to be of approximately expert caliber, and is currently the strongest computer bridge program in the world.

Connected through hyperedge HE-1 (induced by source paper "survey of artificial intelligence for card games and its application to the swiss game jass"):
  with members:
    - "improving search with supervised learning in trick based card games"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "deep counterfactual regret minimization"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "application of self play reinforcement learning to a four player game of imperfect information"
    - "traditional wisdom and monte carlo tree search face to face in the card game scopone"
    - "evaluating and modelling hanabi playing agents"

Connected through hyperedge HE-2 (induced by source paper "re determinizing information set monte carlo tree search in hanabi"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "evolving agents for the hanabi 2018 cig competition"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "evaluating and modelling hanabi playing agents"
    - "combining prediction of human decisions with ismcts in imperfect information games"
    - "thinking fast and slow with deep learning and tree search"
    - "hanabi is np hard even for cheaters who look at their cards"

Connected through hyperedge HE-3 (induced by source paper "learning policies from human data for skat"):
  with members:
    - "improving search with supervised learning in trick based card games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "competitive bridge bidding with deep neural networks"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "automatic bridge bidding using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "policy based inference in trick taking card games"):
  with members:
    - "learning policies from human data for skat"
    - "improving search with supervised learning in trick based card games"

Connected through hyperedge HE-6 (induced by source paper "the alpha mu search algorithm for the game of bridge"):
  with members:
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"

Connected through hyperedge HE-7 (induced by source paper "improving search with supervised learning in trick based card games"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "combining prediction of human decisions with ismcts in imperfect information games"):
  with members:
    - "opponent modeling in deep reinforcement learning"
    - "deep learning in neural networks"
    - "computing approximate nash equilibria and robust best responses using sampling"

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.AI(Artificial Intelligence)