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 "intrinsic dynamic shape prior for fast sequential and dense non rigid structure from motion with detection of temporally disjoint rigidity"):
  Abstract: While dense non-rigid structure from motion (NRSfM) has been extensively studied from the perspective of the reconstructability problem over the recent years, almost no attempts have been undertaken to bring it into the practical realm. The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios. To fill this gap, we propose a hybrid approach that extracts prior shape knowledge from an input sequence with NRSfM and uses it as a dynamic shape prior for sequential surface recovery in scenarios with recurrence. Our Dynamic Shape Prior Reconstruction (DSPR) method can be combined with existing dense NRSfM techniques while its energy functional is optimised with stochastic gradient descent at real-time rates for new incoming point tracks. The proposed versatile framework with a new core NRSfM approach outperforms several other methods in the ability to handle inaccurate and noisy point tracks, provided we have access to a representative (in terms of the deformation variety) image sequence. Comprehensive experiments highlight convergence properties and the accuracy of DSPR under different disturbing effects. We also perform a joint study of tracking and reconstruction and show applications to shape compression and heart reconstruction under occlusions. We achieve state-of-the-art metrics (accuracy and compression ratios) in different scenarios.

Includes members:
  Member "ismo gan adversarial learning for monocular non rigid 3d reconstruction":
    also connected through other hyperedges:
      (none)
  Member "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "eventcap monocular 3d capture of high speed human motions using an event camera")
  Member "a benchmark and evaluation of non rigid structure from motion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a simple prior free method for non rigid structure from motion factorization revisited")
  Member "structure from recurrent motion from rigidity to recurrency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting temporal context for 3d human pose estimation in the wild")
  Member "geometry aware network for non rigid shape prediction from a single view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3dpeople modeling the geometry of dressed humans")
  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 "hdm net monocular non rigid 3d reconstruction with learned deformation model")
  Member "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "scalable dense monocular surface reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data")

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)