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: "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
  Abstract: Finding correspondences between images or 3D scans is at the heart of many computer vision and image retrieval applications and is often enabled by matching local keypoint descriptors. Various learning approaches have been applied in the past to different stages of the matching pipeline, considering detector, descriptor, or metric learning objectives. These objectives were typically addressed separately and most previous work has focused on image data. This paper proposes an end-to-end learning framework for keypoint detection and its representation (descriptor) for 3D depth maps or 3D scans, where the two can be jointly optimized towards task-specific objectives without a need for separate annotations. We employ a Siamese architecture augmented by a sampling layer and a novel score loss function which in turn affects the selection of region proposals. The positive and negative examples are obtained automatically by sampling corresponding region proposals based on their consistency with known 3D pose labels. Matching experiments with depth data on multiple benchmark datasets demonstrate the efficacy of the proposed approach, showing significant improvements over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "point voxel cnn for efficient 3d deep learning"
    - "elf embedded localisation of features in pre trained cnn"
    - "deep closest point learning representations for point cloud registration"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-2 (induced by source paper "scale adaptive neural dense features learning via hierarchical context aggregation"):
  with members:
    - "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "pairwise relational networks for face recognition"
    - "the contextual loss for image transformation with non aligned data"
    - "deep metric learning with hierarchical triplet loss"
    - "pyramid stereo matching network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-3 (induced by source paper "viewsynth learning local features from depth using view synthesis"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "deeply supervised depth map super resolution as novel view synthesis"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning local rgb to cad correspondences for object pose estimation"
    - "extreme view synthesis"
    - "sampling matters in deep embedding learning"
    - "benchmarking 6dof urban visual localization in changing conditions"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning to find good correspondences"

Connected through hyperedge HE-5 (induced by source paper "deepicp an end to end deep neural network for 3d point cloud registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "learning depth with convolutional spatial propagation network"
    - "deeptam deep tracking and mapping"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "imls slam scan to model matching based on 3d data"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-6 (induced by source paper "skd unsupervised keypoint detecting for point clouds using embedded saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "elf embedded localisation of features in pre trained cnn"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "generating 3d adversarial point clouds"

Connected through hyperedge HE-7 (induced by source paper "mist multiple instance spatial transformer networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "linearized multi sampling for differentiable image transformation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "lf net learning local features from images"
    - "multi attention multi class constraint for fine grained image recognition"
    - "self produced guidance for weakly supervised object localization"

Connected through hyperedge HE-8 (induced by source paper "learning local rgb to cad correspondences for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"

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)