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: "mt vae learning motion transformations to generate multimodal human dynamics"
  Abstract: Long-term human motion can be represented as a series of motion modes---motion sequences that capture short-term temporal dynamics---with transitions between them. We leverage this structure and present a novel Motion Transformation Variational Auto-Encoders (MT-VAE) for learning motion sequence generation. Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode. Our model is able to generate multiple diverse and plausible motion sequences in the future from the same input. We apply our approach to both facial and full body motion, and demonstrate applications like analogy-based motion transfer and video synthesis.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"
    - "video generation from text"

Connected through hyperedge HE-3 (induced by source paper "point to point video generation"):
  with members:
    - "diversity sensitive conditional generative adversarial networks"
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "self supervised learning of a facial attribute embedding from video"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-4 (induced by source paper "predicting 3d human dynamics from video"):
  with members:
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "deep lip reading a comparison of models and an online application"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "learning 3d human dynamics from video"
    - "flow grounded spatial temporal video prediction from still images"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning of object structure and dynamics from videos"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "unsupervised learning of object keypoints for perception and control"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "unsupervised discovery of parts structure and dynamics"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-6 (induced by source paper "compositional video prediction"):
  with members:
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "stochastic video generation with a learned prior"
    - "relational inductive biases deep learning and graph networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "interpretable intuitive physics model"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "visual interaction networks"

Connected through hyperedge HE-7 (induced by source paper "imitation learning for human pose prediction"):
  with members:
    - "procedure planning in instructional videos"
    - "peeking into the future predicting future person activities and locations in videos"
    - "action agnostic human pose forecasting"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning plannable representations with causal infogan"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-8 (induced by source paper "dancing to music"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "self supervised audio spatialization with correspondence classifier"
    - "video to video synthesis"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "audio visual scene analysis with self supervised multisensory features"

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)