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: "innateness alphazero and artificial intelligence"
  Abstract: The concept of innateness is rarely discussed in the context of artificial intelligence. When it is discussed, or hinted at, it is often the context of trying to reduce the amount of innate machinery in a given system. In this paper, I consider as a test case a recent series of papers by Silver et al (Silver et al., 2017a) on AlphaGo and its successors that have been presented as an argument that a "even in the most challenging of domains: it is possible to train to superhuman level, without human examples or guidance", "starting tabula rasa." #R##N#I argue that these claims are overstated, for multiple reasons. I close by arguing that artificial intelligence needs greater attention to innateness, and I point to some proposals about what that innateness might look like.

Connected through hyperedge HE-1 (induced by source paper "neo a learned query optimizer"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "skinnerdb regret bounded query evaluation via reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning scheduling algorithms for data processing clusters"
    - "learning to optimize join queries with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "leveling the playing field fairness in ai versus human game benchmarks"):
  with members:
    - "generative design in minecraft gdmc settlement generation competition"
    - "starcraft ii a new challenge for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "measuring intelligence through games"

Connected through hyperedge HE-3 (induced by source paper "evolving self supervised neural networks autonomous intelligence from evolved self teaching"):
  with members:
    - "evolving self taught neural networks the baldwin effect and the emergence of intelligence"
    - "meta learning by the baldwin effect"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning for pedestrians backpropagation in cnns"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "neural arithmetic logic units"
    - "darts differentiable architecture search"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "revisiting small batch training for deep neural networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "human like generalization in a machine through predicate learning"):
  with members:
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
    - "deep learning a critical appraisal"
    - "overcoming catastrophic forgetting in neural networks"
    - "towards deep symbolic reinforcement learning"
    - "building machines that learn and think like people"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"):
  with members:
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "a group theoretic approach to abstraction hierarchical interpretable and task free clustering"):
  with members:
    - "probabilistic rule realization and selection"

Connected through hyperedge HE-8 (induced by source paper "a group theoretic approach to computational abstraction symmetry driven hierarchical clustering"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

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)