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: "autoencoder augmented neuroevolution for visual doom playing"
  Abstract: Neuroevolution has proven effective at many reinforcement learning tasks, but does not seem to scale well to high-dimensional controller representations, which are needed for tasks where the input is raw pixel data. We propose a novel method where we train an autoencoder to create a comparatively low-dimensional representation of the environment observation, and then use CMA-ES to train neural network controllers acting on this input data. As the behavior of the agent changes the nature of the input data, the autoencoder training progresses throughout evolution. We test this method in the VizDoom environment built on the classic FPS Doom, where it performs well on a health-pack gathering task.

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"
    - "safe mutations for deep and recurrent neural networks through output gradients"

Connected through hyperedge HE-2 (induced by source paper "can increasing input dimensionality improve deep reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "decoupling dynamics and reward for transfer learning"
    - "sim to real transfer learning using robustified controllers in robotic tasks involving complex dynamics"
    - "self normalizing neural networks"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-3 (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"
    - "safe mutations for deep and recurrent neural networks through output gradients"

Connected through hyperedge HE-4 (induced by source paper "self adapting goals allow transfer of predictive models to new tasks"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "recurrent world models facilitate policy evolution"
    - "predicting deeper into the future of semantic segmentation"
    - "learning to generate long term future via hierarchical prediction"
    - "imagination augmented agents for deep reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "learning to act by predicting the future"

Connected through hyperedge HE-5 (induced by source paper "recurrent world models facilitate policy evolution"):
  with members:
    - "one big net for everything"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "visual interaction networks"
    - "attention is all you need"
    - "unsupervised learning of disentangled representations from video"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a benchmark environment motivated by industrial control problems"

Connected through hyperedge HE-6 (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"
    - "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"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "deep unsupervised multi view detection of video game stream highlights"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "a deep convolutional auto encoder with pooling unpooling layers in caffe"
    - "convolutional two stream network fusion for video action recognition"
    - "learning representations of affect from speech"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "very deep convolutional networks for large scale image recognition"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "playing atari with six neurons"):
  with members:
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "rainbow combining improvements in deep reinforcement learning"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep learning for video game playing"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "a survey of sparse representation algorithms and applications"
    - "sparse modeling for image and vision processing"
    - "neuroevolution in games state of the art and open challenges"

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.AI(Artificial Intelligence)