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: "giraffe using deep reinforcement learning to play chess"
  Abstract: This report presents Giraffe, a chess engine that uses self-play to discover all its domain-specific knowledge, with minimal hand-crafted knowledge given by the programmer. Unlike previous attempts using machine learning only to perform parameter-tuning on hand-crafted evaluation functions, Giraffe's learning system also performs automatic feature extraction and pattern recognition. The trained evaluation function performs comparably to the evaluation functions of state-of-the-art chess engines - all of which containing thousands of lines of carefully hand-crafted pattern recognizers, tuned over many years by both computer chess experts and human chess masters. Giraffe is the most successful attempt thus far at using end-to-end machine learning to play chess.

Connected through hyperedge HE-1 (induced by source paper "multiagent evaluation under incomplete information"):
  with members:
    - "emergent coordination through competition"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "pagerank optimization by edge selection"
    - "learning equilibria of games via payoff queries"

Connected through hyperedge HE-2 (induced by source paper "reinforcing classical planning for adversary driving scenarios"):
  with members:
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "carla an open urban driving simulator"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "socially aware motion planning with deep reinforcement learning"
    - "long term planning by short term prediction"
    - "end to end learning for self driving cars"
    - "the option critic architecture"

Connected through hyperedge HE-3 (induced by source paper "single step options for adversary driving"):
  with members:
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "carla an open urban driving simulator"
    - "socially aware motion planning with deep reinforcement learning"
    - "long term planning by short term prediction"
    - "end to end learning for self driving cars"
    - "the option critic architecture"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "sentimate learning to play chess through natural language processing"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "exploring the performance of deep residual networks in crazyhouse chess"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "deep pepper expert iteration based chess agent in the reinforcement learning setting"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on the convergence of adam and beyond"
    - "searching for activation functions"
    - "thinking fast and slow with deep learning and tree search"
    - "memory augmented control networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "how to combine tree search methods in reinforcement learning"):
  with members:
    - "multiple step greedy policies in online and approximate reinforcement learning"
    - "is q learning provably efficient"
    - "feedback based tree search for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "asynchronous methods for deep reinforcement learning"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"

Connected through hyperedge HE-8 (induced by source paper "passgan a deep learning approach for password guessing"):
  with members:
    - "cgans with projection discriminator"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "improving gan training via binarized representation entropy bre regularization"
    - "activation maximization generative adversarial nets"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "the numerics of gans"

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)