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: "faster and better a machine learning approach to corner detection"
  Abstract: The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.

Connected through hyperedge HE-1 (induced by source paper "a comparative study for non rigid image registration and rigid image registration"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "non rigid image registration using self supervised fully convolutional networks without training data"
    - "an unsupervised learning model for deformable medical image registration"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "understanding crowd flow movements using active langevin model"):
  with members:
    - "estimation of linear motion in dense crowd videos using langevin model"
    - "a diffusion and clustering based approach for finding coherent motions and understanding crowd scenes"
    - "crowd behavior analysis"
    - "crowd flow segmentation in compressed domain using crf"
    - "detection of salient regions in crowded scenes"

Connected through hyperedge HE-3 (induced by source paper "estimating a null model of scientific image reuse to support research integrity investigations"):
  with members:
    - "lf net learning local features from images"
    - "image matching using sift surf brief and orb performance comparison for distorted images"

Connected through hyperedge HE-4 (induced by source paper "leveraging photogrammetric mesh models for aerial ground feature point matching toward integrated 3d reconstruction"):
  with members:
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-5 (induced by source paper "self supervised 3d keypoint learning for ego motion estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "visual odometry revisited what should be learnt"
    - "neural outlier rejection for self supervised keypoint learning"

Connected through hyperedge HE-6 (induced by source paper "high level semantic feature detection a new perspective for pedestrian detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-7 (induced by source paper "center and scale prediction a box free approach for pedestrian and face detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "deep retinex decomposition for low light enhancement"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "recent progress in semantic image segmentation"):
  with members:
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "dilated residual networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "real time facial segmentation and performance capture from rgb input"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)