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: "cross modal deep variational hand pose estimation"
  Abstract: The human hand moves in complex and high-dimensional ways, making estimation of 3D hand pose configurations from images alone a challenging task. In this work we propose a method to learn a statistical hand model represented by a cross-modal trained latent space via a generative deep neural network. We derive an objective function from the variational lower bound of the VAE framework and jointly optimize the resulting cross-modal KL-divergence and the posterior reconstruction objective, naturally admitting a training regime that leads to a coherent latent space across multiple modalities such as RGB images, 2D keypoint detections or 3D hand configurations. Additionally, it grants a straightforward way of using semi-supervision. This latent space can be directly used to estimate 3D hand poses from RGB images, outperforming the state-of-the art in different settings. Furthermore, we show that our proposed method can be used without changes on depth images and performs comparably to specialized methods. Finally, the model is fully generative and can synthesize consistent pairs of hand configurations across modalities. We evaluate our method on both RGB and depth datasets and analyze the latent space qualitatively.

Connected through hyperedge HE-1 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation"):
  with members:
    - "adaptive graphical model network for 2d handpose estimation"
    - "3d hand shape and pose estimation from a single rgb image"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "3d hand shape and pose from images in the wild"
    - "deep high resolution representation learning for human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"

Connected through hyperedge HE-2 (induced by source paper "3d hand pose estimation in the wild via graph refinement under adversarial learning"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "single image 3d hand reconstruction with mesh convolutions"
    - "deepgcns can gcns go as deep as cnns"
    - "disentangling latent hands for image synthesis and pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"

Connected through hyperedge HE-4 (induced by source paper "unsupervised robust disentangling of latent characteristics for image synthesis"):
  with members:
    - "a two step disentanglement method"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "unsupervised part based disentangling of object shape and appearance"
    - "synthesizing images of humans in unseen poses"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multimodal unsupervised image to image translation"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-5 (induced by source paper "end to end hand mesh recovery from a monocular rgb image"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "face alignment in full pose range a 3d total solution"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-6 (induced by source paper "monocular 3d human pose estimation by generation and ordinal ranking"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "a variational u net for conditional appearance and shape generation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "exploiting temporal information for 3d human pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"

Connected through hyperedge HE-7 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "analysis of hand segmentation in the wild"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-8 (induced by source paper "learning joint reconstruction of hands and manipulated objects"):
  with members:
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"

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)