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: "neural kinematic networks for unsupervised motion retargetting"
  Abstract: We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.

Connected through hyperedge HE-1 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "deep video based performance cloning"
    - "video based reconstruction of 3d people models"
    - "sfv reinforcement learning of physical skills from videos"

Connected through hyperedge HE-2 (induced by source paper "deep differentiable grasp planner for high dof grippers"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "new formulation of mixed integer conic programming for globally optimal grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "difftaichi differentiable programming for physical simulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "music2dance music driven dance generation using wavenet"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "onsets and frames dual objective piano transcription"
    - "on human motion prediction using recurrent neural networks"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"
    - "madmom a new python audio and music signal processing library"

Connected through hyperedge HE-4 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-5 (induced by source paper "predicting 3d human dynamics from video"):
  with members:
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "deep lip reading a comparison of models and an online application"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "learning 3d human dynamics from video"
    - "flow grounded spatial temporal video prediction from still images"

Connected through hyperedge HE-6 (induced by source paper "learning to sit synthesizing human chair interactions via hierarchical control"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "convolutional sequence to sequence model for human dynamics"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sfv reinforcement learning of physical skills from videos"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning human motion models for long term predictions"
    - "deep representation learning for human motion prediction and classification"

Connected through hyperedge HE-7 (induced by source paper "structedit learning structural shape variations"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "learning to infer and execute 3d shape programs"
    - "3dn 3d deformation network"
    - "meshcnn a network with an edge"
    - "3d point cloud generative adversarial network based on tree structured graph convolutions"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "learning free form deformations for 3d object reconstruction"

Connected through hyperedge HE-8 (induced by source paper "learning character agnostic motion for motion retargeting in 2d"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "on the convergence of adam and beyond"
    - "deep video based performance cloning"
    - "sfv reinforcement learning of physical skills from videos"
    - "everybody dance now"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "end to end recovery of human shape and pose"
    - "mocogan decomposing motion and content for video generation"

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)