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: "efficient global point cloud alignment using bayesian nonparametric mixtures"
  Abstract: Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap.

Connected through hyperedge HE-1 (induced by source paper "can generalised relative pose estimation solve sparse 3d registration"):
  with members:
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "imls slam scan to model matching based on 3d data"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "learning compact geometric features"
    - "gogma globally optimal gaussian mixture alignment"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "multi image matching via fast alternating minimization"

Connected through hyperedge HE-2 (induced by source paper "certifiably globally optimal extrinsic calibration from per sensor egomotion"):
  with members:
    - "self calibration of mobile manipulator kinematic and sensor extrinsic parameters through contact based interaction"
    - "rotation averaging and strong duality"
    - "entropy based sim 3 calibration of 2d lidars to egomotion sensors"
    - "a certifiably correct algorithm for synchronization over the special euclidean group"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"
    - "hand eye and robot world calibration by global polynomial optimization"

Connected through hyperedge HE-3 (induced by source paper "a new method for atlanta world frame estimation"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "multi class model fitting by energy minimization and mode seeking"
    - "robust and globally optimal manhattan frame estimation in near real time"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-4 (induced by source paper "a novel method for the absolute pose problem with pairwise constraints"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "deterministic consensus maximization with biconvex programming"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "an efficient algebraic solution to the perspective three point problem"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-5 (induced by source paper "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "geometric loss functions for camera pose regression with deep learning"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "gogma globally optimal gaussian mixture alignment"

Connected through hyperedge HE-6 (induced by source paper "3d registration of curves and surfaces using local differential information"):
  with members:
    - "gogma globally optimal gaussian mixture alignment"
    - "go icp a globally optimal solution to 3d icp point set registration"

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)