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 "keep it smpl automatic estimation of 3d human pose and shape from a single image"):
  Abstract: We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.

Includes members:
  Member "sparse representation for 3d shape estimation a convex relaxation approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ego pose estimation and forecasting as real time pd control")
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "sparseness meets deepness 3d human pose estimation from monocular video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning")
  Member "deepcut joint subset partition and labeling for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "fast and robust hand tracking using detection guided optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")
  Member "a dual source approach for 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hemlets posh learning part centric heatmap triplets for 3d human pose and shape estimation")
  Member "flowing convnets for human pose estimation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "direct prediction of 3d body poses from motion compensated sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention a lightweight 2d hand pose estimation approach")

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)