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: "continuous markov random fields for robust stereo estimation"
  Abstract: In this paper we present a novel slanted-plane MRF model which reasons jointly about occlusion boundaries as well as depth. We formulate the problem as the one of inference in a hybrid MRF composed of both continuous (i.e., slanted 3D planes) and discrete (i.e., occlusion boundaries) random variables. This allows us to define potentials encoding the ownership of the pixels that compose the boundary between segments, as well as potentials encoding which junctions are physically possible. Our approach outperforms the state-of-the-art on Middlebury high resolution imagery as well as in the more challenging KITTI dataset, while being more efficient than existing slanted plane MRF-based methods, taking on average 2 minutes to perform inference on high resolution imagery.

Connected through hyperedge HE-1 (induced by source paper "lego learning edge with geometry all at once by watching videos"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"

Connected through hyperedge HE-2 (induced by source paper "stereoscopic neural style transfer"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "continuous 3d label stereo matching using local expansion moves"
    - "towards open set identity preserving face synthesis"
    - "learning selfie friendly abstraction from artistic style images"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "characterizing and improving stability in neural style transfer"
    - "revisiting deep intrinsic image decompositions"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-3 (induced by source paper "learning to generate facial depth maps"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "poseidon face from depth for driver pose estimation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "learning representations for automatic colorization"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"

Connected through hyperedge HE-4 (induced by source paper "cbmv a coalesced bidirectional matching volume for disparity estimation"):
  with members:
    - "continuous 3d label stereo matching using local expansion moves"
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "end to end training of hybrid cnn crf models for stereo"
    - "computing the stereo matching cost with a convolutional neural network"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-5 (induced by source paper "continuous 3d label stereo matching using local expansion moves"):
  with members:
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-6 (induced by source paper "semi global stereo matching with surface orientation priors"):
  with members:
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-7 (induced by source paper "low level vision by consensus in a spatial hierarchy of regions"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "from shading to local shape"
    - "cross scale cost aggregation for stereo matching"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-8 (induced by source paper "depth map prediction from a single image using a multi scale deep network"):
  with members:
    - "unsupervised learning of depth and motion"
    - "depth extraction from video using non parametric sampling"

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)