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 "hdm net monocular non rigid 3d reconstruction with learned deformation model"):
  Abstract: Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision. Current techniques either require dense correspondences and rely on motion and deformation cues, or assume a highly accurate reconstruction (referred to as a template) of at least a single frame given in advance and operate in the manner of non-rigid tracking. Accurate computation of dense point tracks often requires multiple frames and might be computationally expensive. Availability of a template is a very strong prior which restricts system operation to a pre-defined environment and scenarios. In this work, we propose a new hybrid approach for monocular non-rigid reconstruction which we call Hybrid Deformation Model Network (HDM-Net). In our approach, deformation model is learned by a deep neural network, with a combination of domain-specific loss functions. We train the network with multiple states of a non-rigidly deforming structure with a known shape at rest. HDM-Net learns different reconstruction cues including texture-dependent surface deformations, shading and contours. We show generalisability of HDM-Net to states not presented in the training dataset, with unseen textures and under new illumination conditions. Experiments with noisy data and a comparison with other methods demonstrate robustness and accuracy of the proposed approach and suggest possible application scenarios of the new technique in interventional diagnostics and augmented reality.

Includes members:
  Member "geometry aware network for non rigid shape prediction from a single view":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3dpeople modeling the geometry of dressed humans")
  Member "monocap monocular human motion capture using a cnn coupled with a geometric prior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unipose unified human pose estimation in single images and videos")
  Member "octnetfusion learning depth fusion from data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "implicit functions in feature space for 3d shape reconstruction and completion")
  Member "large pose 3d face reconstruction from a single image via direct volumetric cnn regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")
  Member "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intrinsic dynamic shape prior for fast sequential and dense non rigid structure from motion with detection of temporally disjoint rigidity")
  Member "end to end 3d face reconstruction with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "training efficient network architecture and weights via direct sparsity control")
  Member "better together joint reasoning for non rigid 3d reconstruction with specularities and shading":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion")
  Member "lifting from the deep convolutional 3d pose estimation from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised 3d human pose learning via multi view images in the wild")

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)