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: "style transfer generative adversarial networks learning to play chess differently"
  Abstract: The idea of style transfer has largely only been explored in image-based tasks, which we attribute in part to the specific nature of loss functions used for style transfer. We propose a general formulation of style transfer as an extension of generative adversarial networks, by using a discriminator to regularize a generator with an otherwise separate loss function. We apply our approach to the task of learning to play chess in the style of a specific player, and present empirical evidence for the viability of our approach.

Connected through hyperedge HE-1 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generative adversarial parallelization"
    - "multi generator generative adversarial nets"
    - "revisiting distributed synchronous sgd"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "invertible conditional gans for image editing"
    - "generative multi adversarial networks"
    - "towards geo distributed machine learning"

Connected through hyperedge HE-2 (induced by source paper "a zero shot framework for sketch based image retrieval"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "a simple exponential family framework for zero shot learning"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "zero shot learning the good the bad and the ugly"
    - "fast zero shot image tagging"
    - "generative adversarial text to image synthesis"

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)