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: "learning individual styles of conversational gesture"
  Abstract: Human speech is often accompanied by hand and arm gestures. Given audio speech input, we generate plausible gestures to go along with the sound. Specifically, we perform cross-modal translation from "in-the-wild'' monologue speech of a single speaker to their hand and arm motion. We train on unlabeled videos for which we only have noisy pseudo ground truth from an automatic pose detection system. Our proposed model significantly outperforms baseline methods in a quantitative comparison. To support research toward obtaining a computational understanding of the relationship between gesture and speech, we release a large video dataset of person-specific gestures. The project website with video, code and data can be found at this http URL .

Connected through hyperedge HE-1 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders"):
  with members:
    - "stargan vc2 rethinking conditional methods for stargan based voice conversion"
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "text based editing of talking head video"
    - "speech2face learning the face behind a voice"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"

Connected through hyperedge HE-2 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "dancing to music"
    - "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-3 (induced by source paper "alignnet a unifying approach to audio visual alignment"):
  with members:
    - "the sound of motions"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "action agnostic human pose forecasting"
    - "perfect match improved cross modal embeddings for audio visual synchronisation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "dynamic temporal alignment of speech to lips"
    - "voxceleb2 deep speaker recognition"
    - "the sound of pixels"

Connected through hyperedge HE-4 (induced by source paper "batvision learning to see 3d spatial layout with two ears"):
  with members:
    - "audio visual model distillation using acoustic images"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "soundnet learning sound representations from unlabeled video"
    - "u net convolutional networks for biomedical image segmentation"

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)