human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning joint reconstruction of hands and manipulated objects"
  Abstract: Estimating hand-object manipulations is essential for interpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation. Yet, reconstructing hands and objects during manipulation is a more challenging task due to significant occlusions of both the hand and object. While presenting challenges, manipulations may also simplify the problem since the physics of contact restricts the space of valid hand-object configurations. For example, during manipulation, the hand and object should be in contact but not interpenetrate. In this work, we regularize the joint reconstruction of hands and objects with manipulation constraints. We present an end-to-end learnable model that exploits a novel contact loss that favors physically plausible hand-object constellations. Our approach improves grasp quality metrics over baselines, using RGB images as input. To train and evaluate the model, we also propose a new large-scale synthetic dataset, ObMan, with hand-object manipulations. We demonstrate the transferability of ObMan-trained models to real data.

Connected through hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands"):
  with members:
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "scene level pose estimation for multiple instances of densely packed objects"
    - "manipulation by feel touch based control with deep predictive models"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "principal components of touch"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-2 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-3 (induced by source paper "human grasp classification for reactive human to robot handovers"):
  with members:
    - "transferable task execution from pixels through deep planning domain learning"
    - "3d hand shape and pose estimation from a single rgb image"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "representing robot task plans as robust logical dynamical systems"
    - "honnotate a method for 3d annotation of hand and objects poses"
    - "online replanning in belief space for partially observable task and motion problems"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"

Connected through hyperedge HE-4 (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  with members:
    - "temporally coherent full 3d mesh human pose recovery from monocular video"
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "learning to train with synthetic humans"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "virtualhome simulating household activities via programs"
    - "human action recognition and prediction a survey"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-5 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "monocular total capture posing face body and hands in the wild"

Connected through hyperedge HE-6 (induced by source paper "h o unified egocentric recognition of 3d hand object poses and interactions"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"
    - "object level visual reasoning in videos"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "detecting and recognizing human object interactions"
    - "learning to estimate 3d hand pose from single rgb images"
    - "from lifestyle vlogs to everyday interactions"

Connected through hyperedge HE-7 (induced by source paper "single image 3d hand reconstruction with mesh convolutions"):
  with members:
    - "meshgan non linear 3d morphable models of faces"
    - "neural 3d morphable models spiral convolutional networks for 3d shape representation learning and generation"
    - "end to end hand mesh recovery from a monocular rgb image"
    - "3d hand shape and pose from images in the wild"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "densereg fully convolutional dense shape regression in the wild"

Connected through hyperedge HE-8 (induced by source paper "freihand a dataset for markerless capture of hand pose and shape from single rgb images"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "large scale multiview 3d hand pose dataset"
    - "learning to estimate 3d hand pose from single rgb images"
    - "learning a multi view stereo machine"

Question: Please tell me which class the center node 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)