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 character agnostic motion for motion retargeting in 2d"
  Abstract: Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer animation, is the retargeting of motion from one performer to another. While humans move in three dimensions, the vast majority of human motions are captured using video, requiring 2D-to-3D pose and camera recovery, before existing retargeting approaches may be applied. In this paper, we present a new method for retargeting video-captured motion between different human performers, without the need to explicitly reconstruct 3D poses and/or camera parameters.#R##N##R##N#In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view. Our key idea is to train a deep neural network to decompose temporal sequences of 2D poses into three components: motion, skeleton, and camera view-angle. Having extracted such a representation, we are able to re-combine motion with novel skeletons and camera views, and decode a retargeted temporal sequence, which we compare to a ground truth from a synthetic dataset.#R##N##R##N#We demonstrate that our framework can be used to robustly extract human motion from videos, bypassing 3D reconstruction, and outperforming existing retargeting methods, when applied to videos in-the-wild. It also enables additional applications, such as performance cloning, video-driven cartoons, and motion retrieval.

Connected through hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans"):
  with members:
    - "design automation for efficient deep learning computing"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "semantic image synthesis with spatially adaptive normalization"
    - "co evolutionary compression for unpaired image translation"
    - "compressing gans using knowledge distillation"
    - "video to video synthesis"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints"):
  with members:
    - "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"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "video based reconstruction of 3d people models"
    - "sfv reinforcement learning of physical skills from videos"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of object structure and dynamics from videos"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "unsupervised discovery of parts structure and dynamics"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "mt vae learning motion transformations to generate multimodal human dynamics"

Connected through hyperedge HE-4 (induced by source paper "dancing to music"):
  with members:
    - "self supervised audio spatialization with correspondence classifier"
    - "video to video synthesis"
    - "learning to localize sound source in visual scenes"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "audio visual scene analysis with self supervised multisensory features"

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)