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 "registration of multi view point sets under the perspective of expectation maximization"):
  Abstract: Registration of multi-view point sets is a prerequisite for 3D model reconstruction. To solve this problem, most of previous approaches either partially explore available information or blindly utilize unnecessary information to align each point set, which may lead to the undesired results or introduce extra computation complexity. To this end, this paper consider the multi-view registration problem as a maximum likelihood estimation problem and proposes a novel multi-view registration approach under the perspective of Expectation-Maximization (EM). The basic idea of our approach is that different data points are generated by the same number of Gaussian mixture models (GMMs). For each data point in one well-aligned point set, its nearest neighbors can be searched from other well-aligned point sets to explore more available information. Then, we can suppose this data point is generated by the special GMM, which is composed of each of its nearest neighbor adhered with one Gaussian distribution. Based on this assumption, it is reasonable to define the likelihood function, which contains all rigid transformations required to be estimated for multi-view registration. Subsequently, the EM algorithm is utilized to maximize the likelihood function so as to estimate all rigid transformations. Finally, the proposed approach is tested on several bench mark data sets and compared with some state-of-the-art algorithms. Experimental results illustrate its super performance on accuracy and efficiency for the registration of multi-view point sets.

Includes members:
  Member "simultaneous merging multiple grid maps using the robust motion averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collaborative slam based on wifi fingerprint similarity and motion information")
  Member "joint alignment of multiple point sets with batch and incremental expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation")
  Member "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kpam sc generalizable manipulation planning using keypoint affordance and shape completion")
  Member "weighted motion averaging for the registration of multi view range scans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi view registration based on weighted low rank and sparse matrix decomposition of motions")
  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 "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "point set registration coherent point drift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "on the closed form solution of the rotation matrix arising in computer vision problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions")

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)