human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  Abstract: Deep generative models are often used for human motion prediction as they are able to model multi-modal data distributions and characterize diverse human behavior. While much care has been taken into designing and learning deep generative models, how to efficiently produce diverse samples from a deep generative model after it has been trained is still an under-explored problem. To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples. Clearly, this random sampling strategy is not guaranteed to produce diverse samples for two reasons: (1) The independent sampling cannot force the samples to be diverse; (2) The sampling is based solely on likelihood which may only produce samples that correspond to the major modes of the data distribution. To address these problems, we propose a novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse set of samples from a pretrained deep generative model. Unlike random (independent) sampling, the proposed DLow sampling method samples a single random variable and then maps it with a set of learnable mapping functions to a set of correlated latent codes. The correlated latent codes are then decoded into a set of correlated samples. During training, DLow uses a diversity-promoting prior over samples as an objective to optimize the latent mappings to improve sample diversity. The design of the prior is highly flexible and can be customized to generate diverse motions with common features (e.g., similar leg motion but diverse upper-body motion). Our experiments demonstrate that DLow outperforms state-of-the-art baseline methods in terms of sample diversity and accuracy. Video: https://youtu.be/64OEdSadb00.

Includes members:
  Member "joint 3d tracking and forecasting with graph neural network and diversity sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points")
  Member "lagging inference networks and posterior collapse in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "learning variations in human motion via mix and match perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling good latent variables via cpp vaes vaes with condition posterior as prior")
  Member "diverse trajectory forecasting with determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "learning trajectory dependencies for human motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction")
  Member "structured prediction helps 3d human motion modelling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vibe video inference for human body pose and shape estimation")
  Member "imitation learning for human pose prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction")
  Member "predicting 3d human dynamics from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "post estimation smoothing a simple baseline for learning with side information")

Question: Please tell me which class the source paper 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)