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: "image based synthesis for deep 3d human pose estimation"
  Abstract: This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations. We introduce an image-based synthesis engine that artificially augments a dataset of real images with 2D human pose annotations using 3D motion capture data. Given a candidate 3D pose, our algorithm selects for each joint an image whose 2D pose locally matches the projected 3D pose. The selected images are then combined to generate a new synthetic image by stitching local image patches in a kinematically constrained manner. The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation. We cluster the training data into a large number of pose classes and tackle pose estimation as a $K$-way classification problem. Such an approach is viable only with large training sets such as ours. Our method outperforms most of the published works in terms of 3D pose estimation in controlled environments (Human3.6M) and shows promising results for real-world images (LSP). This demonstrates that CNNs trained on artificial images generalize well to real images. Compared to data generated from more classical rendering engines, our synthetic images do not require any domain adaptation or fine-tuning stage.

Connected through hyperedge HE-1 (induced by source paper "learning to train with synthetic humans"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "learning human optical flow"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "learning to refine human pose estimation"
    - "a closed form solution to photorealistic image stylization"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "how robust is 3d human pose estimation to occlusion"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "exploiting temporal context for 3d human pose estimation in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "structure from recurrent motion from rigidity to recurrency"
    - "learning 3d human dynamics from video"
    - "how robust is 3d human pose estimation to occlusion"
    - "sfv reinforcement learning of physical skills from videos"
    - "mosculp interactive visualization of shape and time"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-3 (induced by source paper "moulding humans non parametric 3d human shape estimation from single images"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "group normalization"
    - "bodynet volumetric inference of 3d human body shapes"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "volumetric performance capture from minimal camera viewpoints"
    - "densepose dense human pose estimation in the wild"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "single shot multi person 3d pose estimation from monocular rgb"

Connected through hyperedge HE-4 (induced by source paper "dgpose deep generative models for human body analysis"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised person image synthesis in arbitrary poses"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "unsupervised discovery of object landmarks as structural representations"
    - "dense pose transfer"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "everybody dance now"

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)