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: "augmented skeleton space transfer for depth based hand pose estimation"
  Abstract: Crucial to the success of training a depth-based 3D hand pose estimator (HPE) is the availability of comprehensive datasets covering diverse camera perspectives, shapes, and pose variations. However, collecting such annotated datasets is challenging. We propose to complete existing databases by generating new database entries. The key idea is to synthesize data in the skeleton space (instead of doing so in the depth-map space) which enables an easy and intuitive way of manipulating data entries. Since the skeleton entries generated in this way do not have the corresponding depth map entries, we exploit them by training a separate hand pose generator (HPG) which synthesizes the depth map from the skeleton entries. By training the HPG and HPE in a single unified optimization framework enforcing that 1) the HPE agrees with the paired depth and skeleton entries; and 2) the HPG-HPE combination satisfies the cyclic consistency (both the input and the output of HPG-HPE are skeletons) observed via the newly generated unpaired skeletons, our algorithm constructs a HPE which is robust to variations that go beyond the coverage of the existing database. Our training algorithm adopts the generative adversarial networks (GAN) training process. As a by-product, we obtain a hand pose discriminator (HPD) that is capable of picking out realistic hand poses. Our algorithm exploits this capability to refine the initial skeleton estimates in testing, further improving the accuracy. We test our algorithm on four challenging benchmark datasets (ICVL, MSRA, NYU and Big Hand 2.2M datasets) and demonstrate that our approach outperforms or is on par with state-of-the-art methods quantitatively and 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"
    - "hand pose estimation via latent 2 5d heatmap regression"

Connected through hyperedge HE-2 (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"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "unsupervised training for 3d morphable model regression"

Connected through hyperedge HE-3 (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"
    - "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"
    - "cross modal deep variational hand pose estimation"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "disentangling pose from appearance in monochrome hand images"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "disentangling latent hands for image synthesis and pose estimation"
    - "exploring disentangled feature representation beyond face identification"
    - "image to image translation for cross domain disentanglement"
    - "vip cnn visual phrase guided convolutional neural network"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "visual question generation as dual task of visual question answering"

Connected through hyperedge HE-5 (induced by source paper "adaptive graphical model network for 2d handpose estimation"):
  with members:
    - "geometric pose affordance 3d human pose with scene constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "large scale multiview 3d hand pose dataset"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "towards accurate multi person pose estimation in the wild"
    - "dense 3d regression for hand pose estimation"

Connected through hyperedge HE-6 (induced by source paper "sampling strategies for gan synthetic data"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "how good is my gan"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "deep facial expression recognition a survey"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "improving facial attribute prediction using semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "hand pose estimation a survey"):
  with members:
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"

Connected through hyperedge HE-8 (induced by source paper "silhouette net 3d hand pose estimation from silhouettes"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "hand3d hand pose estimation using 3d neural network"
    - "deepprior improving fast and accurate 3d hand pose estimation"
    - "towards good practices for deep 3d hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"

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)