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: "pn net conjoined triple deep network for learning local image descriptors"
  Abstract: In this paper we propose a new approach for learning local descriptors for matching image patches. It has recently been demonstrated that descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Unfortunately their computational complexity is prohibitive for any practical application. We address this problem and propose a CNN based descriptor with improved matching performance, significantly reduced training and execution time, as well as low dimensionality. #R##N#We propose to train the network with triplets of patches that include a positive and negative pairs. To that end we introduce a new loss function that exploits the relations within the triplets. We compare our approach to recently introduced MatchNet and DeepCompare and demonstrate the advantages of our descriptor in terms of performance, memory footprint and speed i.e. when run in GPU, the extraction time of our 128 dimensional feature is comparable to the fastest available binary descriptors such as BRIEF and ORB.

Connected through hyperedge HE-1 (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-2 (induced by source paper "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-3 (induced by source paper "unsuperpoint end to end unsupervised interest point detector and descriptor"):
  with members:
    - "lf net learning local features from images"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "geometric loss functions for camera pose regression with deep learning"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "superpoint self supervised interest point detection and description"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-4 (induced by source paper "framenet learning local canonical frames of 3d surfaces from a single rgb image"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "tangent convolutions for dense prediction in 3d"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-5 (induced by source paper "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "understanding convolution for semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-6 (induced by source paper "explicit spatial encoding for deep local descriptors"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "semi convolutional operators for instance segmentation"
    - "a large dataset for improving patch matching"
    - "understanding and improving kernel local descriptors"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning from videos using temporal coherency deep networks"):
  with members:
    - "transitive invariance for self supervised visual representation learning"
    - "rank pooling for action recognition"
    - "the thumos challenge on action recognition for videos in the wild"
    - "unsupervised learning of visual representations using videos"
    - "a robust and efficient video representation for action recognition"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "facenet a unified embedding for face recognition and clustering"
    - "slow and steady feature analysis higher order temporal coherence in video"

Connected through hyperedge HE-8 (induced by source paper "learning local descriptors by optimizing the keypoint correspondence criterion applications to face matching learning from unlabeled videos and 3d shape retrieval"):
  with members:
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "unsupervised learning of visual representations using videos"
    - "tilde a temporally invariant learned detector"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "orb slam a versatile and accurate monocular slam system"
    - "3d shape estimation from 2d landmarks a convex relaxation approach"

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)