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: "large scale evaluation of local image feature detectors on homography datasets"
  Abstract: We present a large scale benchmark for the evaluation of local feature detectors. Our key innovation is the introduction of a new evaluation protocol which extends and improves the standard detection repeatability measure. The new protocol is better for assessment on a large number of images and reduces the dependency of the results on unwanted distractors such as the number of detected features and the feature magnification factor. Additionally, our protocol provides a comprehensive assessment of the expected performance of detectors under several practical scenarios. Using images from the recently-introduced HPatches dataset, we evaluate a range of state-of-the-art local feature detectors on two main tasks: viewpoint and illumination invariant detection. Contrary to previous detector evaluations, our study contains an order of magnitude more image sequences, resulting in a quantitative evaluation significantly more robust to over-fitting. We also show that traditional detectors are still very competitive when compared to recent deep-learning alternatives.

Connected through hyperedge HE-1 (induced by source paper "from handcrafted to deep local features"):
  with members:
    - "local descriptors optimized for average precision"
    - "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"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-2 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "matchable image retrieval by learning from surface reconstruction"
    - "fix your classifier the marginal value of training the last weight layer"
    - "local descriptors optimized for average precision"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "repeatability is not enough learning affine regions via discriminability"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "key net keypoint detection by handcrafted and learned cnn filters"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "lf net learning local features from images"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "superpoint self supervised interest point detection and description"
    - "repeatability is not enough learning affine regions via discriminability"
    - "toward geometric deep slam"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "learning to find good correspondences"

Connected through hyperedge HE-4 (induced by source paper "gift learning transformation invariant dense visual descriptors via group cnns"):
  with members:
    - "equivariant multi view networks"
    - "equivariant transformer networks"
    - "an improved learning framework for covariant local feature detection"
    - "local descriptors optimized for average precision"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "graph based isometry invariant representation learning"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-5 (induced by source paper "from handcrafted to deep local invariant features"):
  with members:
    - "local descriptors optimized for average precision"
    - "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"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-6 (induced by source paper "sips succinct interest points from unsupervised inlierness probability learning"):
  with members:
    - "lf net learning local features from images"
    - "neighbourhood consensus networks"
    - "interest point detectors stability evaluation on apolloscape dataset"
    - "superpoint self supervised interest point detection and description"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "learning to find good correspondences"

Connected through hyperedge HE-7 (induced by source paper "matching features without descriptors implicitly matched interest points imips"):
  with members:
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "direct sparse odometry"
    - "learning covariant feature detectors"
    - "spatial transformer networks"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning to compare image patches via convolutional neural networks"

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)