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: "alphastar an evolutionary computation perspective"
  Abstract: In January 2019, DeepMind revealed AlphaStar to the world---the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II---representing a milestone in the progress of AI. AlphaStar draws on many areas of AI research, including deep learning, reinforcement learning, game theory, and evolutionary computation (EC). In this paper we analyze AlphaStar primarily through the lens of EC, presenting a new look at the system and relating it to many concepts in the field. We highlight some of its most interesting aspects---the use of Lamarckian evolution, competitive co-evolution, and quality diversity. In doing so, we hope to provide a bridge between the wider EC community and one of the most significant AI systems developed in recent times.

Connected through hyperedge HE-1 (induced by source paper "enhanced poet open ended reinforcement learning through unbounded invention of learning challenges and their solutions"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "go explore a new approach for hard exploration problems"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "soft hindsight experience replay"):
  with members:
    - "maximum entropy regularized multi goal reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "if maxent rl is the answer what is the question"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "curiosity driven experience prioritization via density estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"

Connected through hyperedge HE-3 (induced by source paper "formulazero distributionally robust online adaptation via offline population synthesis"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "adversarial policies attacking deep reinforcement learning"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "online control with adversarial disturbances"
    - "efficient black box assessment of autonomous vehicle safety"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "jointly learnable behavior and trajectory planning for self driving vehicles"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-4 (induced by source paper "meta reinforcement learning from observational data"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "an introduction to variational autoencoders"
    - "meta reinforcement learning as task inference"
    - "mapping state space using landmarks for universal goal reaching"
    - "benchmarking model based reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "striving for simplicity in off policy deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "procedural content generation through quality diversity"
    - "evaluating map elites on constrained optimization problems"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"

Connected through hyperedge HE-6 (induced by source paper "model free deep reinforcement learning for urban autonomous driving"):
  with members:
    - "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"
    - "soft actor critic algorithms and applications"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-7 (induced by source paper "pre training as batch meta reinforcement learning with time"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "an introduction to variational autoencoders"
    - "mapping state space using landmarks for universal goal reaching"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "fast context adaptation via meta learning"
    - "off policy deep reinforcement learning without exploration"
    - "deep sets"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-8 (induced by source paper "from crystallized adaptivity to fluid adaptivity in deep reinforcement learning insights from biological systems on adaptive flexibility"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "the value function polytope in reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "addressing function approximation error in actor critic methods"
    - "the predictron end to end learning and planning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

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.NE(Neural and Evolutionary Computing)