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: "deep neuroevolution of recurrent and discrete world models"
  Abstract: Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning (RL) methods in a variety of different domains. Instead of the relatively simple architectures employed in most RL experiments, world models rely on multiple different neural components that are responsible for visual information processing, memory, and decision-making. However, so far the components of these models have to be trained separately and through a variety of specialized training methods. This paper demonstrates the surprising finding that models with the same precise parts can be instead efficiently trained end-to-end through a genetic algorithm (GA), reaching a comparable performance to the original world model by solving a challenging car racing task. An analysis of the evolved visual and memory system indicates that they include a similar effective representation to the system trained through gradient descent. Additionally, in contrast to gradient descent methods that struggle with discrete variables, GAs also work directly with such representations, opening up opportunities for classical planning in latent space. This paper adds additional evidence on the effectiveness of deep neuroevolution for tasks that require the intricate orchestration of multiple components in complex heterogeneous architectures.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-2 (induced by source paper "improving deep neuroevolution via deep innovation protection"):
  with members:
    - "weight agnostic neural networks"
    - "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"
    - "safe mutations for deep and recurrent neural networks through output gradients"

Connected through hyperedge HE-3 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "videoflow a flow based generative model for video"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"
    - "woulda coulda shoulda counterfactually guided policy search"

Connected through hyperedge HE-4 (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"
    - "evolvability es scalable and direct optimization of evolvability"
    - "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"
    - "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-5 (induced by source paper "reinforcement learning with convolutional reservoir computing"):
  with members:
    - "effect of shapes of activation functions on predictability in the echo state network"
    - "weight agnostic neural networks"
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "distributed prioritized experience replay"
    - "recurrent world models facilitate policy evolution"
    - "deep reinforcement learning a brief survey"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "convolutional reservoir computing for world models"):
  with members:
    - "effect of shapes of activation functions on predictability in the echo state network"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "distributed prioritized experience replay"
    - "recurrent world models facilitate policy evolution"
    - "deep reinforcement learning a brief survey"
    - "asynchronous methods for deep reinforcement learning"
    - "the cma evolution strategy a tutorial"

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)