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: "affine subspace representation for feature description"
  Abstract: This paper proposes a novel Affine Subspace Representation (ASR) descriptor to deal with affine distortions induced by viewpoint changes. Unlike the traditional local descriptors such as SIFT, ASR inherently encodes local information of multi-view patches, making it robust to affine distortions while maintaining a high discriminative ability. To this end, PCA is used to represent affine-warped patches as PCA-patch vectors for its compactness and efficiency. Then according to the subspace assumption, which implies that the PCA-patch vectors of various affine-warped patches of the same keypoint can be represented by a low-dimensional linear subspace, the ASR descriptor is obtained by using a simple subspace-to-point mapping. Such a linear subspace representation could accurately capture the underlying information of a keypoint (local structure) under multiple views without sacrificing its distinctiveness. To accelerate the computation of ASR descriptor, a fast approximate algorithm is proposed by moving the most computational part (ie, warp patch under various affine transformations) to an offline training stage. Experimental results show that ASR is not only better than the state-of-the-art descriptors under various image transformations, but also performs well without a dedicated affine invariant detector when dealing with viewpoint changes.

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 "df slam a deep learning enhanced visual slam system based on deep local features"):
  with members:
    - "lf net learning local features from images"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "local descriptors optimized for average precision"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "superpoint self supervised interest point detection and description"
    - "repeatability is not enough learning affine regions via discriminability"
    - "unsupervised learning of depth and ego motion from video"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-3 (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"

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 "scalable solution for approximate nearest subspace search"):
  with members:
    - "spatio temporal covariance descriptors for action and gesture recognition"

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)