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: "descriptor matching with convolutional neural networks a comparison to sift"
  Abstract: Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching. This paper has been merged with arXiv:1406.6909

Connected through hyperedge HE-1 (induced by source paper "learning transformation aware embeddings for image forensics"):
  with members:
    - "learning rich features for image manipulation detection"
    - "classification is a strong baseline for deep metric learning"
    - "fighting fake news image splice detection via learned self consistency"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "image processing operations identification via convolutional neural network"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "a patchmatch based dense field algorithm for video copy move detection and localization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "elf embedded localisation of features in pre trained cnn"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "lf net learning local features from images"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "smoothgrad removing noise by adding noise"
    - "superpoint self supervised interest point detection and description"
    - "axiomatic attribution for deep networks"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-4 (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"
    - "wxbs wide baseline stereo generalizations"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "training deep neural networks to detect repeatable 2d features using large amounts of 3d world capture data"):
  with members:
    - "lf net learning local features from images"
    - "gibson env real world perception for embodied agents"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "matterport3d learning from rgb d data in indoor environments"
    - "lift learned invariant feature transform"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "glampoints greedily learned accurate match points"):
  with members:
    - "lf net learning local features from images"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "in the saddle chasing fast and repeatable features"
    - "u net convolutional networks for biomedical image segmentation"
    - "tilde a temporally invariant learned detector"
    - "learning to assign orientations to feature points"

Connected through hyperedge HE-7 (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-8 (induced by source paper "prototypicality effects in global semantic description of objects"):
  with members:
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "lift learned invariant feature transform"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "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)