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: "evolving simple programs for playing atari games"
  Abstract: Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-2 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "efficacy of modern neuro evolutionary strategies for continuous control optimization"):
  with members:
    - "robust optimization through neuroevolution"
    - "learning dexterous in hand manipulation"
    - "deep reinforcement learning that matters"
    - "towards generalization and simplicity in continuous control"
    - "proximal policy optimization algorithms"
    - "improved techniques for training gans"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "comparing knowledge based reinforcement learning to neural networks in a strategy game"):
  with members:
    - "playing a strategy game with knowledge based reinforcement learning"
    - "model free model based and general intelligence"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep reinforcement learning with double q learning"
    - "learning to win by reading manuals in a monte carlo framework"
    - "intelligent techniques for resolving conflicts of knowledge in multi agent decision support systems"

Connected through hyperedge HE-5 (induced by source paper "2019 evolutionary algorithms review"):
  with members:
    - "guided evolutionary strategies escaping the curse of dimensionality in random search"
    - "markov brains a technical introduction"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "gene expression programming a new adaptive algorithm for solving problems"

Connected through hyperedge HE-6 (induced by source paper "positional cartesian genetic programming"):
  with members:
    - "linear combination of distance measures for surrogate models in genetic programming"
    - "differentiable genetic programming"

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)