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: "deeply supervised depth map super resolution as novel view synthesis"
  Abstract: Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN-based depth map super-resolution methods that hinder the performance: 1) the low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used and 2) the supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as    $\times 8$    and    $\times 16$   . In this paper, we propose a new framework to tackle the above problems. First, we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks. The novel view synthesis sub-task aims at generating (synthesizing) a depth map from a different camera pose, which could be learned in parallel. Second, to handle large up-sampling factors, we present a deeply supervised network structure to enforce strong supervision in each stage of the network. Third, a multi-scale fusion strategy is proposed to effectively exploit the feature maps at different scales and handle the blocking effect. In this way, our proposed framework could deal with challenging depth map super-resolution efficiently under large up-sampling factors (e.g.,    $\times 8$    and    $\times 16$   ). Our method only uses the low-resolution depth map as input, and the support of color image is not needed, which greatly reduces the restriction of our method. Extensive experiments on various benchmarking data sets demonstrate the superiority of our method over current state-of-the-art depth map super-resolution methods.

Connected through hyperedge HE-1 (induced by source paper "viewsynth learning local features from depth using view synthesis"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning local rgb to cad correspondences for object pose estimation"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "extreme view synthesis"
    - "sampling matters in deep embedding learning"
    - "benchmarking 6dof urban visual localization in changing conditions"

Connected through hyperedge HE-2 (induced by source paper "pag net progressive attention guided depth super resolution network"):
  with members:
    - "residual dense network for image super resolution"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "a joint intensity and depth co sparse analysis model for depth map super resolution"

Connected through hyperedge HE-3 (induced by source paper "perceptual deep depth super resolution"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "joint convolutional neural pyramid for depth map super resolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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)