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: "look wider to match image patches with convolutional neural networks"
  Abstract: When a human matches two images, the viewer has a natural tendency to view the wide area around the target pixel to obtain clues of right correspondence. However, designing a matching cost function that works on a large window in the same way is difficult. The cost function is typically not intelligent enough to discard the information irrelevant to the target pixel, resulting in undesirable artifacts. In this letter, we propose a novel convolutional neural network (CNN) module to learn a stereo matching cost with a large-sized window. Unlike conventional pooling layers with strides, the proposed per-pixel pyramid-pooling layer can cover a large area without a loss of resolution and detail. Therefore, the learned matching cost function can successfully utilize the information from a large area without introducing the fattening effect. The proposed method is robust despite the presence of weak textures, depth discontinuity, illumination, and exposure difference. The proposed method achieves near-peak performance on the Middlebury benchmark.

Connected through hyperedge HE-1 (induced by source paper "end to end learning of multi scale convolutional neural network for stereo matching"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"

Connected through hyperedge HE-2 (induced by source paper "cbmv a coalesced bidirectional matching volume for disparity estimation"):
  with members:
    - "continuous 3d label stereo matching using local expansion moves"
    - "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"
    - "the fast bilateral solver"

Connected through hyperedge HE-3 (induced by source paper "semi dense stereo matching using dual cnns"):
  with members:
    - "open world stereo video matching with deep rnn"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "cbmv a coalesced bidirectional matching volume for disparity estimation"
    - "intel realsense stereoscopic depth cameras"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning in remote sensing a review"):
  with members:
    - "toward seamless multiview scene analysis from satellite to street level"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "lift learned invariant feature transform"
    - "a survey on object detection in optical remote sensing images"
    - "gvnn neural network library for geometric computer vision"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-5 (induced by source paper "a learning based framework for hybrid depth from defocus and stereo matching"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "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"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to compare image patches via convolutional neural networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "learning for disparity estimation through feature constancy"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "self supervised learning for stereo matching with self improving ability"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "densely connected convolutional networks"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "deep depth inference using binocular and monocular cues"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "computing the stereo matching cost with a convolutional neural network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "variational depth from focus reconstruction"
    - "learning to compare image patches via convolutional neural networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "widening siamese architectures for stereo matching"):
  with members:
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "end to end learning of geometry and context for deep stereo regression"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "end to end training of hybrid cnn crf models for stereo"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "flownet learning optical flow with convolutional networks"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

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)