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: "toward game level generation from gameplay videos"
  Abstract: Algorithms that generate computer game content require game design knowledge. We present an approach to automatically learn game design knowledge for level design from gameplay videos. We further demonstrate how the acquired design knowledge can be used to generate sections of game levels. Our approach involves parsing video of people playing a game to detect the appearance of patterns of sprites and utilizing machine learning to build a probabilistic model of sprite placement. We show how rich game design information can be automatically parsed from gameplay videos and represented as a set of generative probabilistic models. We use Super Mario Bros. as a proof of concept. We evaluate our approach on a measure of playability and stylistic similarity to the original levels as represented in the gameplay videos.

Connected through hyperedge HE-1 (induced by source paper "conceptual game expansion"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "automatic game design via mechanic generation"
    - "monte carlo tree search for simulation based strategy analysis"
    - "world models"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "automated game design via conceptual expansion"
    - "automatic mapping of nes games with mappy"

Connected through hyperedge HE-2 (induced by source paper "prioritized guidance for efficient multi agent reinforcement learning exploration"):
  with members:
    - "learning latent state representation for speeding up exploration"
    - "deep communicating agents for abstractive summarization"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "proximal policy optimization algorithms"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "data driven design a case for maximalist game design"):
  with members:
    - "data agent"
    - "who killed albert einstein from open data to murder mystery games"
    - "onsets and frames dual objective piano transcription"
    - "the vglc the video game level corpus"

Connected through hyperedge HE-4 (induced by source paper "automated game design learning"):
  with members:
    - "charda causal hybrid automata recovery via dynamic analysis"
    - "curiosity driven exploration by self supervised prediction"
    - "mechanics automatically recognized via interactive observation jumping"
    - "the vglc the video game level corpus"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-5 (induced by source paper "super mario as a string platformer level generation via lstms"):
  with members:
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"
    - "translating videos to natural language using deep recurrent neural networks"
    - "learning to execute"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "the vglc the video game level corpus"):
  with members:
    - "super mario as a string platformer level generation via lstms"
    - "a neural algorithm of artistic style"

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)