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: "forward modeling for partial observation strategy games a starcraft defogger"
  Abstract: We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games. We propose to employ encoder-decoder neural networks for this task, and introduce proxy tasks and baselines for evaluation to assess their ability of capturing basic game rules and high-level dynamics. By combining convolutional neural networks and recurrent networks, we exploit spatial and sequential correlations and train well-performing models on a large dataset of human games of StarCraft: Brood War. Finally, we demonstrate the relevance of our models to downstream tasks by applying them for enemy unit prediction in a state-of-the-art, rule-based StarCraft bot. We observe improvements in win rates against several strong community bots.

Connected through hyperedge HE-1 (induced by source paper "defoggan predicting hidden information in the starcraft fog of war with generative adversarial nets"):
  with members:
    - "single image haze removal using conditional wasserstein generative adversarial networks"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "clear the fog combat value assessment in incomplete information games with convolutional encoder decoders"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "improved training of wasserstein gans"
    - "stardata a starcraft ai research dataset"

Connected through hyperedge HE-2 (induced by source paper "pytorch an imperative style high performance deep learning library"):
  with members:
    - "starcraft ii a new challenge for reinforcement learning"
    - "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
    - "automatic differentiation in machine learning a survey"
    - "fast algorithms for convolutional neural networks"
    - "julia a fresh approach to numerical computing"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-3 (induced by source paper "high level strategy selection under partial observability in starcraft brood war"):
  with members:
    - "learning macromanagement in starcraft from replays using deep learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "adam a method for stochastic optimization"

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)