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: "wxbs wide baseline stereo generalizations"
  Abstract: We have presented a new problem -- the wide multiple baseline stereo (WxBS) -- which considers matching of images that simultaneously differ in more than one image acquisition factor such as viewpoint, illumination, sensor type or where object appearance changes significantly, e.g. over time. A new dataset with the ground truth for evaluation of matching algorithms has been introduced and will be made public. #R##N#We have extensively tested a large set of popular and recent detectors and descriptors and show than the combination of RootSIFT and HalfRootSIFT as descriptors with MSER and Hessian-Affine detectors works best for many different nuisance factors. We show that simple adaptive thresholding improves Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them on infrared and low contrast images. #R##N#A novel matching algorithm for addressing the WxBS problem has been introduced. We have shown experimentally that the WxBS-M matcher dominantes the state-of-the-art methods both on both the new and existing datasets.

Connected through hyperedge HE-1 (induced by source paper "from handcrafted to deep local features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-2 (induced by source paper "leveraging outdoor webcams for local descriptor learning"):
  with members:
    - "matchable image retrieval by learning from surface reconstruction"
    - "from handcrafted to deep local invariant features"
    - "local descriptors optimized for average precision"
    - "learning a local feature descriptor for 3d lidar scans"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "a large dataset for improving patch matching"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-3 (induced by source paper "robust angular local descriptor learning"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "sampling matters in deep embedding learning"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning deep embeddings with histogram loss"
    - "facenet a unified embedding for face recognition and clustering"
    - "deephash getting regularization depth and fine tuning right"

Connected through hyperedge HE-4 (induced by source paper "from handcrafted to deep local invariant features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "lift learned invariant feature transform"
    - "densely connected convolutional networks"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-5 (induced by source paper "understanding and improving kernel local descriptors"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "multiple kernel local patch descriptor"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "visualizing deep convolutional neural networks using natural pre images"

Connected through hyperedge HE-6 (induced by source paper "geodesc learning local descriptors by integrating geometry constraints"):
  with members:
    - "a large dataset for improving patch matching"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "no fuss distance metric learning using proxies"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "learning local descriptors by optimizing the keypoint correspondence criterion"

Connected through hyperedge HE-7 (induced by source paper "deep spectral correspondence for matching disparate image pairs"):
  with members:
    - "superpoint self supervised interest point detection and description"
    - "toward geometric deep slam"
    - "lift learned invariant feature transform"
    - "learning to compare image patches via convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "two view matching with view synthesis revisited"

Connected through hyperedge HE-8 (induced by source paper "large scale evaluation of local image feature detectors on homography datasets"):
  with members:
    - "superpoint self supervised interest point detection and description"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "learning covariant feature detectors"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "faster and better a machine learning approach to corner detection"

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)