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: "stardata a starcraft ai research dataset"
  Abstract: We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset. We make the dataset available at this https URL . En Taro Adun!

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"
    - "forward modeling for partial observation strategy games a starcraft defogger"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "minerl a large scale dataset of minecraft demonstrations"):
  with members:
    - "learning dexterous in hand manipulation"
    - "rainbow combining improvements in deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the atari grand challenge dataset"
    - "interactive 3d modeling with a generative adversarial network"
    - "hierarchical and interpretable skill acquisition in multi task reinforcement learning"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-3 (induced by source paper "forward modeling for partial observation strategy games a starcraft defogger"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"
    - "learning physical intuition of block towers by example"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-4 (induced by source paper "deep rts a game environment for deep reinforcement learning in real time strategy games"):
  with members:
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep reinforcement learning an overview"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "learning to navigate in complex environments"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "msc a dataset for macro management in starcraft ii"):
  with members:
    - "learning macromanagement in starcraft from replays using deep learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "asynchronous methods for deep reinforcement learning"
    - "playing fps games with deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural turing machines"

Connected through hyperedge HE-6 (induced by source paper "robocupsimdata a robocup soccer research dataset"):
  with members:
    - "analysing soccer games with clustering and conceptors"

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)