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: "joint alignment of multiple point sets with batch and incremental expectation maximization"
  Abstract: This paper addresses the problem of registering multiple point sets. Solutions to this problem are often approximated by repeatedly solving for pairwise registration, which results in an uneven treatment of the sets forming a pair: a model set and a data set. The main drawback of this strategy is that the model set may contain noise and outliers, which negatively affects the estimation of the registration parameters. In contrast, the proposed formulation treats all the point sets on an equal footing. Indeed, all the points are drawn from a central Gaussian mixture, hence the registration is cast into a clustering problem. We formally derive batch and incremental EM algorithms that robustly estimate both the GMM parameters and the rotations and translations that optimally align the sets. Moreover, the mixture's means play the role of the registered set of points while the variances provide rich information about the contribution of each component to the alignment. We thoroughly test the proposed algorithms on simulated data and on challenging real data collected with range sensors. We compare them with several state-of-the-art algorithms, and we show their potential for surface reconstruction from depth data.

Connected through hyperedge HE-1 (induced by source paper "registration of multi view point sets under the perspective of expectation maximization"):
  with members:
    - "simultaneous merging multiple grid maps using the robust motion averaging"
    - "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization"
    - "weighted motion averaging for the registration of multi view range scans"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "point set registration coherent point drift"
    - "on the closed form solution of the rotation matrix arising in computer vision problems"

Connected through hyperedge HE-2 (induced by source paper "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"
    - "generative cooperative net for image generation and data augmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "improved training of wasserstein gans"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-3 (induced by source paper "fast registration for cross source point clouds by using weak regional affinity and pixel wise refinement"):
  with members:
    - "a systematic approach for cross source point cloud registration by preserving macro and micro structures"
    - "a coarse to fine algorithm for registration in 3d street view cross source point clouds"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "point set registration coherent point drift"

Connected through hyperedge HE-4 (induced by source paper "robust feature based point registration using directional mixture model"):
  with members:
    - "delio decoupled lidar odometry"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "point set registration coherent point drift"

Connected through hyperedge HE-5 (induced by source paper "efficient and robust registration on the 3d special euclidean group"):
  with members:
    - "density adaptive point set registration"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-6 (induced by source paper "fast and accurate point cloud registration using trees of gaussian mixtures"):
  with members:
    - "an adaptive data representation for robust point set registration and merging"
    - "point set registration coherent point drift"
    - "outlier robust icp for minimizing fractional rmsd"

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)