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: "dance dance generation motion transfer for internet videos"
  Abstract: This work presents computational methods for transferring body movements from one person to another with videos collected in the wild. Specifically, we train a personalized model on a single video from the Internet which can generate videos of this target person driven by the motions of other people. Our model is built on two generative networks: a human (foreground) synthesis net which generates photo-realistic imagery of the target person in a novel pose, and a fusion net which combines the generated foreground with the scene (background), adding shadows or reflections as needed to enhance realism. We validate the the efficacy of our proposed models over baselines with qualitative and quantitative evaluations as well as a subjective test.

Connected through hyperedge HE-1 (induced by source paper "few shot video to video synthesis"):
  with members:
    - "video generation from single semantic label map"
    - "pixel adaptive convolutional neural networks"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "vid2game controllable characters extracted from real world videos"
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "adversarial video generation on complex datasets"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "generating diverse high fidelity images with vq vae 2"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "pose guided human video generation"

Connected through hyperedge HE-3 (induced by source paper "music oriented dance video synthesis with pose perceptual loss"):
  with members:
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep video generation prediction and completion of human action sequences"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "gac gan a general method for appearance controllable human video motion transfer"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "example guided style consistent image synthesis from semantic labeling"
    - "cgans with projection discriminator"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "deep video based performance cloning"
    - "pcgan partition controlled human image generation"

Connected through hyperedge HE-5 (induced by source paper "metapix few shot video retargeting"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "few shot adversarial learning of realistic neural talking head models"
    - "video to video synthesis"
    - "on first order meta learning algorithms"
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "dense pose transfer"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "appearance composing gan a general method for appearance controllable human video motion transfer"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "example guided style consistent image synthesis from semantic labeling"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "instance level human parsing via part grouping network"
    - "deep video based performance cloning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

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)