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 "hand keypoint detection in single images using multiview bootstrapping"):
  Abstract: We present an approach that uses a multi-camera system to train fine-grained detectors for keypoints that are prone to occlusion, such as the joints of a hand. We call this procedure multiview bootstrapping: first, an initial keypoint detector is used to produce noisy labels in multiple views of the hand. The noisy detections are then triangulated in 3D using multiview geometry or marked as outliers. Finally, the reprojected triangulations are used as new labeled training data to improve the detector. We repeat this process, generating more labeled data in each iteration. We derive a result analytically relating the minimum number of views to achieve target true and false positive rates for a given detector. The method is used to train a hand keypoint detector for single images. The resulting keypoint detector runs in realtime on RGB images and has accuracy comparable to methods that use depth sensors. The single view detector, triangulated over multiple views, enables 3D markerless hand motion capture with complex object interactions.

Includes members:
  Member "convolutional pose machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "training a feedback loop for hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering")
  Member "efficiently creating 3d training data for fine hand pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dual grid net hand mesh vertex regression from single depth maps")
  Member "capturing hands in action using discriminative salient points and physics simulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning multi human optical flow")
  Member "realtime multi person 2d pose estimation using part affinity fields":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "real time joint tracking of a hand manipulating an object from rgb d input":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a technology aided multi modal training approach to assist abdominal palpation training and its assessment in medical education")
  Member "fast and robust hand tracking using detection guided optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")

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)