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: "elf embedded localisation of features in pre trained cnn"
  Abstract: This paper introduces a novel feature detector based only on information embedded inside a CNN trained on standard tasks (e.g. classification). While previous works already show that the features of a trained CNN are suitable descriptors, we show here how to extract the feature locations from the network to build a detector. This information is computed from the gradient of the feature map with respect to the input image. This provides a saliency map with local maxima on relevant keypoint locations. Contrary to recent CNN-based detectors, this method requires neither supervised training nor finetuning. We evaluate how repeatable and how matchable the detected keypoints are with the repeatability and matching scores. Matchability is measured with a simple descriptor introduced for the sake of the evaluation. This novel detector reaches similar performances on the standard evaluation HPatches dataset, as well as comparable robustness against illumination and viewpoint changes on Webcam and photo-tourism images. These results show that a CNN trained on a standard task embeds feature location information that is as relevant as when the CNN is specifically trained for feature detection.

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"
    - "deep closest point learning representations for point cloud registration"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (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"
    - "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"
    - "geodesc learning local descriptors by integrating geometry constraints"

Connected through hyperedge HE-3 (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"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "generating 3d adversarial point clouds"

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)