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 "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences"):
  Abstract: This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings. In these settings, methods that directly handle input point sets are preferable. Without correspondences, however, conventional randomized techniques require a very large number of samples in order to reach satisfactory solutions. In this paper, we propose a novel approach to address this problem. In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences. By considering point cloud alignment as a special instance of graph matching and employing an efficient semi-definite relaxation, we propose a novel sampling mechanism, in which the size of the sampled subsets can be larger-than-minimal. Our tight relaxation scheme enables fast rejection of the outliers in the sampled sets, resulting in high-quality hypotheses. We conduct extensive experiments to demonstrate that our approach outperforms other state-of-the-art methods. Importantly, our proposed method serves as a generic framework which can be extended to problems with known correspondences.

Includes members:
  Member "on device scalable image based localization via prioritized cascade search and fast one many ransac":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning")
  Member "deterministic consensus maximization with biconvex programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection")
  Member "maximum consensus parameter estimation by reweighted ell_1 methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consensus maximization tree search revisited")
  Member "ds a flexible scalable and provably tight relaxation for matching problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "go icp a globally optimal solution to 3d icp point set registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "alternating direction graph matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "outlier robust icp for minimizing fractional rmsd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "least squares registration of point sets over se d using closed form projections")

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)