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: "dancing to music"
  Abstract: Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move. In the synthesis phase, the model learns how to compose a dance by organizing multiple basic dancing movements seamlessly according to the input music. Experimental qualitative and quantitative results demonstrate that the proposed method can synthesize realistic, diverse,style-consistent, and beat-matching dances from music.

Connected through hyperedge HE-1 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "learning individual styles of conversational gesture"
    - "a neural temporal model for human motion prediction"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "convolutional sequence to sequence model for human dynamics"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "neural design network graphic layout generation with constraints"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "generating diverse high fidelity images with vq vae 2"
    - "layoutvae stochastic scene layout generation from a label set"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "graph r cnn for scene graph generation"
    - "a style based generator architecture for generative adversarial networks"
    - "image generation from scene graphs"

Connected through hyperedge HE-3 (induced by source paper "drit diverse image to image translation via disentangled representations"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "exploring explicit domain supervision for latent space disentanglement in unpaired image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "on gans and gmms"

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.CV(Computer Vision and Pattern Recognition)