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: "neuroevolution in games state of the art and open challenges"
  Abstract: This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.

Connected through hyperedge HE-1 (induced by source paper "improving deep neuroevolution via deep innovation protection"):
  with members:
    - "weight agnostic neural networks"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "scalable co optimization of morphology and control in embodied machines"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-2 (induced by source paper "collaborative evolutionary reinforcement learning"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-3 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control"):
  with members:
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "deep reinforcement learning that matters"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (induced by source paper "deep neuroevolution of recurrent and discrete world models"):
  with members:
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "evolving deep neural networks"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-5 (induced by source paper "adaptive genomic evolution of neural network topologies agent for state to action mapping in autonomous agents"):
  with members:
    - "advances in experience replay"
    - "multi criteria evolution of neural network topologies balancing experience and performance in autonomous systems"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "the effects of memory replay in reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "spectrum diverse neuroevolution with unified neural models"
    - "actor critic versus direct policy search a comparison based on sample complexity"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "evolution guided policy gradient in reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "parameter space noise for exploration"

Connected through hyperedge HE-7 (induced by source paper "automated curriculum learning by rewarding temporally rare events"):
  with members:
    - "computational theories of curiosity driven learning"
    - "hindsight experience replay"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "deep learning for video game playing"
    - "starcraft ii a new challenge for reinforcement learning"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "the foundations of deep learning with a path towards general intelligence"):
  with members:
    - "progressive neural networks"
    - "a simple neural network module for relational reasoning"
    - "dynamic routing between capsules"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "geometric deep learning going beyond euclidean data"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "imagination augmented agents for deep reinforcement 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)