human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation"):
  Abstract: In this work we present a novel keypoint detector that uses saliency to determine the best candidates from point clouds for tasks such as registration and reconstruction. The approach can be applied to any differentiable deep learning descriptor by using the gradients of that descriptor with respect to the input to estimate an initial set of candidate keypoints. By using a neural network over the set of candidates we can also learn to refine the point selection until the actual keypoints are obtained. The key intuition behind this approach is that keypoints need to be determined based on how the descriptor behaves given a task and not just because of the geometry that surrounds a point. To improve the performance of the learned keypoint descriptor we combine the saliency, the feature signal and geometric information from the point cloud to allow the network to select good keypoint candidates. The approach was evaluated on two large LIDAR datasets - the Oxford RobotCar dataset and the KITTI datasets, where we obtain up to 50% improvement over the state-of-the-art in both matchability score and repeatability. This results in a higher inlier ratio and a faster registration without compromising metric accuracy.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "usip unsupervised stable interest point detection from 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "pcan 3d attention map learning using contextual information for point cloud based retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geometric back projection network for point cloud classification")
  Member "interpolated convolutional networks for 3d point cloud understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "point voxel cnn for efficient 3d deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advectivenet an eulerian lagrangian fluidic reservoir for point cloud processing")
  Member "elf embedded localisation of features in pre trained cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforced feature points optimizing feature detection and description for a high level task")
  Member "deep closest point learning representations for point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d point cloud processing and learning for autonomous driving")

Question: Please tell me which class the source paper 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)