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: "evolvability es scalable and direct optimization of evolvability"
  Abstract: Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and enables fast adaptation to changing circumstances. This paper introduces evolvability ES, an evolutionary algorithm designed to explicitly and efficiently optimize for evolvability, i.e. the ability to further adapt. The insight is that it is possible to derive a novel objective in the spirit of natural evolution strategies that maximizes the diversity of behaviors exhibited when an individual is subject to random mutations, and that efficiently scales with computation. Experiments in 2-D and 3-D locomotion tasks highlight the potential of evolvability ES to generate solutions with tens of thousands of parameters that can quickly be adapted to solve different tasks and that can productively seed further evolution. We further highlight a connection between evolvability and a recent and popular gradient-based meta-learning algorithm called MAML; results show that evolvability ES can perform competitively with MAML and that it discovers solutions with distinct properties. The conclusion is that evolvability ES opens up novel research directions for studying and exploiting the potential of evolvable representations for deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-2 (induced by source paper "scaling map elites to deep neuroevolution"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "adversarial policies attacking deep reinforcement learning"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "teacher algorithms for curriculum learning of deep rl in continuously parameterized environments"
    - "reset free trial and error learning for robot damage recovery"
    - "exploration by random network distillation"

Connected through hyperedge HE-3 (induced by source paper "es maml simple hessian free meta learning"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "norml no reward meta learning"
    - "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"
    - "deep neuroevolution of recurrent and discrete world models"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "meta learning by the baldwin effect"

Connected through hyperedge HE-4 (induced by source paper "learning to score behaviors for guided policy optimization"):
  with members:
    - "distributional reinforcement learning with linear function approximation"
    - "provably robust blackbox optimization for reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "deepmind control suite"
    - "policy optimization as wasserstein gradient flows"
    - "nonlinear distributional gradient temporal difference learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "recurrent world models facilitate policy evolution"

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)