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: "beating the world s best at super smash bros with deep reinforcement learning"
  Abstract: There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with minimal knowledge of the specific domain of interest. In this work, we will investigate the performance of these methods on Super Smash Bros. Melee (SSBM), a popular console fighting game. The SSBM environment has complex dynamics and partial observability, making it challenging for human and machine alike. The multi-player aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it is possible to train agents that are competitive against and even surpass human professionals, a new result for the multi-player video game setting.

Connected through hyperedge HE-1 (induced by source paper "improving electron micrograph signal to noise with an atrous convolutional encoder decoder"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "revisiting distributed synchronous sgd"

Connected through hyperedge HE-2 (induced by source paper "creating pro level ai for real time fighting game with deep reinforcement learning"):
  with members:
    - "advantage amplification in slowly evolving latent state environments"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep reinforcement learning for playing 2 5d fighting games"
    - "distributed prioritized experience replay"
    - "starcraft ii a new challenge for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "incremental reinforcement learning a new continuous reinforcement learning frame based on stochastic differential equation methods"):
  with members:
    - "proximal policy optimization algorithms"
    - "learning to learn by gradient descent by gradient descent"
    - "benchmarking deep reinforcement learning for continuous control"
    - "deep learning in neural networks"
    - "continuous control with deep reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"
    - "towards vision based deep reinforcement learning for robotic motion control"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "torille learning environment for hand to hand combat"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "vizdoom competitions playing doom from pixels"
    - "unity a general platform for intelligent agents"
    - "diversity is all you need learning skills without a reward function"
    - "learning to walk via deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-5 (induced by source paper "deep in gpu experience replay"):
  with members:
    - "nintendo super smash bros melee an untouchable agent"
    - "dueling network architectures for deep reinforcement learning"
    - "massively parallel methods for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-6 (induced by source paper "at human speed deep reinforcement learning with action delay"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "curiosity driven exploration by self supervised prediction"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-7 (induced by source paper "nintendo super smash bros melee an untouchable agent"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q 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.LG(Machine Learning)