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: "forecasting human dynamics from static images"
  Abstract: This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on single-image human pose estimation and sequence prediction, and converts the 2D predictions into 3D space. We train our 3D-PFNet using a three-step training strategy to leverage a diverse source of training data, including image and video based human pose datasets and 3D motion capture (MoCap) data. We demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and 3D pose recovery through quantitative and qualitative results.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (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-3 (induced by source paper "ego pose estimation and forecasting as real time pd control"):
  with members:
    - "xr egopose egocentric 3d human pose from an hmd camera"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning 3d human dynamics from video"
    - "sfv reinforcement learning of physical skills from videos"
    - "flow grounded spatial temporal video prediction from still images"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "unsupervised learning of disentangled representations from video"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-4 (induced by source paper "chirality nets for human pose regression"):
  with members:
    - "pic permutation invariant critic for multi agent deep reinforcement learning"
    - "unsupervised learning of object structure and dynamics from videos"
    - "on the convergence of adam and beyond"
    - "ordinal depth supervision for 3d human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "action agnostic human pose forecasting"

Connected through hyperedge HE-5 (induced by source paper "visual reaction learning to play catch with your drone"):
  with members:
    - "relational action forecasting"
    - "high fidelity video prediction with large stochastic recurrent neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "semi parametric topological memory for navigation"
    - "learning and querying fast generative models for reinforcement learning"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"

Connected through hyperedge HE-6 (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"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-7 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "human action recognition and prediction a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"

Connected through hyperedge HE-8 (induced by source paper "imitation learning for human pose prediction"):
  with members:
    - "procedure planning in instructional videos"
    - "peeking into the future predicting future person activities and locations in videos"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "action agnostic human pose forecasting"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning plannable representations with causal infogan"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"

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)