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: "single image tomography 3d volumes from 2d cranial x rays"
  Abstract: As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which is then fused in a second step with the input x-ray into a high-resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer-simulated 2D x-ray images of 3D volumes scanned from 175 mammalian species. Applications of our approach include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x-rays.

Connected through hyperedge HE-1 (induced by source paper "escaping plato s cave 3d shape from adversarial rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "deep single view 3d object reconstruction with visual hull embedding"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"
    - "improved training of wasserstein gans"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "net2vis a visual grammar for automatically generating publication ready cnn architecture visualizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "cnncomparator comparative analytics of convolutional neural networks"
    - "ssgan secure steganography based on generative adversarial networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "deeporgannet on the fly reconstruction and visualization of 3d 4d lung models from single view projections by deep deformation network"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "image2mesh a learning framework for single image 3d reconstruction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformnet free form deformation network for 3d shape reconstruction from a single image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-4 (induced by source paper "escaping plato s cave using adversarial training 3d shape from unstructured 2d image collections"):
  with members:
    - "deep single view 3d object reconstruction with visual hull embedding"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "learning representations and generative models for 3d point clouds"
    - "improved training of wasserstein gans"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

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.GR(Graphics)