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: "low level vision by consensus in a spatial hierarchy of regions"
  Abstract: We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs. The framework uses a dense, overlapping set of image regions at multiple scales and a "local model," such as a slanted-plane model for stereo disparity, that is expected to be valid piecewise across the visual field. Estimation is cast as optimization over a dichotomous mixture of variables, simultaneously determining which regions are inliers with respect to the local model (binary variables) and the correct co-ordinates in the local model space for each inlying region (continuous variables). When the regions are organized into a multi-scale hierarchy, optimization can occur in an efficient and parallel architecture, where distributed computational units iteratively perform calculations and share information through sparse connections between parents and children. The framework performs well on a standard benchmark for binocular stereo, and it produces a distributional scene representation that is appropriate for combining with higher-level reasoning and other low-level cues.

Connected through hyperedge HE-1 (induced by source paper "fast deep stereo with 2d convolutional processing of cost signatures"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "learning for disparity estimation through feature constancy"
    - "end to end learning of cost volume aggregation for real time dense stereo"
    - "computing the stereo matching cost with a convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "unrealstereo controlling hazardous factors to analyze stereo vision"):
  with members:
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "adversarial examples for semantic segmentation and object detection"
    - "unrealcv connecting computer vision to unreal engine"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "playing for data ground truth from computer games"
    - "computing the stereo matching cost with a convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "depth from a single image by harmonizing overcomplete local network predictions"):
  with members:
    - "coupled depth learning"
    - "return of the devil in the details delving deep into convolutional nets"
    - "designing deep networks for surface normal estimation"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "fully convolutional networks for semantic segmentation"
    - "depth extraction from video using non parametric sampling"

Connected through hyperedge HE-4 (induced by source paper "semi supervised learning of deep metrics for stereo reconstruction"):
  with members:
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "confidence driven tgv fusion"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "the fast bilateral solver"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-5 (induced by source paper "stereo matching by training a convolutional neural network to compare image patches"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "deepmatching hierarchical deformable dense matching"
    - "learning to compare image patches via convolutional neural networks"
    - "cudnn efficient primitives for deep learning"

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)