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: "domain size pooling in local descriptors dsp sift"
  Abstract: We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.

Connected through hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice"):
  with members:
    - "self supervised learning of depth and ego motion with differentiable bundle adjustment"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "understanding the limitations of cnn based absolute camera pose regression"

Connected through hyperedge HE-2 (induced by source paper "extremely dense point correspondences using a learned feature descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "defslam tracking and mapping of deforming scenes from monocular sequences"
    - "3d reconstruction of whole stomach from endoscope video using structure from motion"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "normface l 2 hypersphere embedding for face verification"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (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-4 (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"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "repeatability is not enough learning affine regions via discriminability"

Connected through hyperedge HE-5 (induced by source paper "d2 net a trainable cnn for joint detection and description of local features"):
  with members:
    - "lf net learning local features from images"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "megadepth learning single view depth prediction from internet photos"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "superpoint self supervised interest point detection and description"
    - "matching neural paths transfer from recognition to correspondence search"
    - "convolutional neural network architecture for geometric matching"
    - "repeatability is not enough learning affine regions via discriminability"

Connected through hyperedge HE-6 (induced by source paper "reinforced feature points optimizing feature detection and description for a high level task"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "elf embedded localisation of features in pre trained cnn"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "lf net learning local features from images"
    - "sips succinct interest points from unsupervised inlierness probability learning"

Connected through hyperedge HE-7 (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"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "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"

Connected through hyperedge HE-8 (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"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "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"

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)