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: "latent ransac"
  Abstract: We present a method that can evaluate a RANSAC hypothesis in constant time, i.e. independent of the size of the data. A key observation here is that correct hypotheses are tightly clustered together in the latent parameter domain. In a manner similar to the generalized Hough transform we seek to find this cluster, only that we need as few as two votes for a successful detection. Rapidly locating such pairs of similar hypotheses is made possible by adapting the recent "Random Grids" range-search technique. We only perform the usual (costly) hypothesis verification stage upon the discovery of a close pair of hypotheses. We show that this event rarely happens for incorrect hypotheses, enabling a significant speedup of the RANSAC pipeline. The suggested approach is applied and tested on three robust estimation problems: camera localization, 3D rigid alignment and 2D-homography estimation. We perform rigorous testing on both synthetic and real datasets, demonstrating an improvement in efficiency without a compromise in accuracy. Furthermore, we achieve state-of-the-art 3D alignment results on the challenging "Redwood" loop-closure challenge.

Connected through hyperedge HE-1 (induced by source paper "learning multiview 3d point cloud registration"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "efficient and robust registration on the 3d special euclidean group"
    - "3d local features for direct pairwise registration"
    - "deep closest point learning representations for point cloud registration"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "the perfect match 3d point cloud matching with smoothed densities"

Connected through hyperedge HE-2 (induced by source paper "zero shot task transfer"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "bayesian model agnostic meta learning"
    - "taskonomy disentangling task transfer learning"
    - "adversarial data programming using gans to relax the bottleneck of curated labeled data"
    - "domain adaptation for visual applications a comprehensive survey"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-3 (induced by source paper "3d local features for direct pairwise registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bop benchmark for 6d object pose estimation"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-4 (induced by source paper "fast regularity constrained plane reconstruction"):
  with members:
    - "searching for representative modes on hypergraphs for robust geometric model fitting"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "robust and globally optimal manhattan frame estimation in near real time"

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)