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 depth estimation by dilated deep residual convolutional neural network and soft weight sum inference"
  Abstract: This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation. Compared with existing deep CNN based methods, our method achieves much better results with fewer training examples and model parameters. The advantages of our method come from the usage of dilated convolution, skip connection architecture and soft-weight-sum inference. Experimental evaluation on the NYU Depth V2 dataset shows that our method outperforms other state-of-the-art methods by a margin.

Connected through hyperedge HE-1 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching"):
  with members:
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "label refinement network for coarse to fine semantic segmentation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-2 (induced by source paper "evaluation of cnn based single image depth estimation methods"):
  with members:
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "deep ordinal regression network for monocular depth estimation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-3 (induced by source paper "playing for depth"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "megadepth learning single view depth prediction from internet photos"
    - "rethinking atrous convolution for semantic image segmentation"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image to image translation with conditional adversarial networks"
    - "playing for data ground truth from computer games"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "depth reconstruction of translucent objects from a single time of flight camera using deep residual networks"):
  with members:
    - "photometric stereo in participating media considering shape dependent forward scatter"
    - "deeptof off the shelf real time correction of multipath interference in time of flight imaging"
    - "learned multi patch similarity"
    - "visual attribute transfer through deep image analogy"
    - "estimating depth from monocular images as classification using deep fully convolutional residual networks"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)