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 "3d local features for direct pairwise registration"):
  Abstract: We present a novel, data driven approach for solving the problem of registration of two point cloud scans. Our approach is direct in the sense that a single pair of corresponding local patches already provides the necessary transformation cue for the global registration. To achieve that, we first endow the state of the art PPF-FoldNet auto-encoder (AE) with a pose-variant sibling, where the discrepancy between the two leads to pose-specific descriptors. Based upon this, we introduce RelativeNet, a relative pose estimation network to assign correspondence-specific orientations to the keypoints, eliminating any local reference frame computations. Finally, we devise a simple yet effective hypothesize-and-verify algorithm to quickly use the predictions and align two point sets. Our extensive quantitative and qualitative experiments suggests that our approach outperforms the state of the art in challenging real datasets of pairwise registration and that augmenting the keypoints with local pose information leads to better generalization and a dramatic speed-up.

Includes members:
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "latent ransac":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning multiview 3d point cloud registration")
  Member "bop benchmark for 6d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "the perfect match 3d point cloud matching with smoothed densities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")
  Member "3dfeat net weakly supervised local 3d features for point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial joint image and pose distribution learning for camera pose regression and refinement")
  Member "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "guaranteed outlier removal for point cloud registration with correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "teaser fast and certifiable point cloud registration")

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)