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: "competitive coevolution through evolutionary complexification"
  Abstract: Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.

Connected through hyperedge HE-1 (induced by source paper "emergent tool use from multi agent autocurricula"):
  with members:
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "structured agents for physical construction"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "growing action spaces"):
  with members:
    - "the starcraft multi agent challenge"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "coegan evaluating the coevolution effect in generative adversarial networks"):
  with members:
    - "coevolution of generative adversarial networks"
    - "evolutionary generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "towards distributed coevolutionary gans"
    - "evolving deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "coevolution of generative adversarial networks"):
  with members:
    - "evolutionary generative adversarial networks"
    - "evolving deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "alphastar an evolutionary computation perspective"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "re evaluating evaluation"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "population based training of neural networks"
    - "quality and diversity optimization a unifying modular framework"

Connected through hyperedge HE-6 (induced by source paper "evolving neural networks to follow trajectories of arbitrary complexity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "progressive neural networks"
    - "hierarchical representations for efficient architecture search"
    - "deep learning in neural networks"
    - "evolution of a subsumption architecture neurocontroller"

Connected through hyperedge HE-7 (induced by source paper "model based stochastic search for large scale optimization of multi agent uav swarms"):
  with members:
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep reinforcement learning an overview"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "the cma evolution strategy a tutorial"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a robot to shape your natural plant the machine learning approach to model and control bio hybrid systems"):
  with members:
    - "flora robotica an architectural system combining living natural plants and distributed robots"
    - "tensorflow a system for large scale machine learning"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

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)