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 "predicting 3d human dynamics from video"):
  Abstract: Given a video of a person in action, we can easily guess the 3D future motion of the person. In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input. We do this for periodic motions such as walking and also actions like bowling and squatting seen in sports or workout videos. While there has been a surge of future prediction problems in computer vision, most approaches predict 3D future from 3D past or 2D future from 2D past inputs. In this work, we focus on the problem of predicting 3D future motion from past image sequences, which has a plethora of practical applications in autonomous systems that must operate safely around people from visual inputs. Inspired by the success of autoregressive models in language modeling tasks, we learn an intermediate latent space on which we predict the future. This effectively facilitates autoregressive predictions when the input differs from the output domain. Our approach can be trained on video sequences obtained in-the-wild without 3D ground truth labels. The project website with videos can be found at this https URL.

Includes members:
  Member "xnect real time multi person 3d human pose estimation with a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning")
  Member "exploiting temporal context for 3d human pose estimation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "sim2real transfer learning for 3d pose estimation motion to the rescue":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "synthetic humans for action recognition from unseen viewpoints")
  Member "deep lip reading a comparison of models and an online application":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pseudo convolutional policy gradient for sequence to sequence lip reading")
  Member "3d human pose estimation in video with temporal convolutions and semi supervised training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "mt vae learning motion transformations to generate multimodal human dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "neural kinematic networks for unsupervised motion retargetting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")
  Member "learning 3d human dynamics from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")

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)