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: "outlier robust icp for minimizing fractional rmsd"
  Abstract: We describe a variation of the iterative closest point (ICP) algorithm for aligning two point sets under a set of transformations. Our algorithm is superior to previous algorithms because (1) in determining the optimal alignment, it identifies and discards likely outliers in a statistically robust manner, and (2) it is guaranteed to converge to a locally optimal solution. To this end, we formalize a new distance measure, fractional root mean squared distance (frmsd), which incorporates the fraction of inliers into the distance function. We lay out a specific implementation, but our framework can easily incorporate most techniques and heuristics from modern registration algorithms. We experimentally validate our algorithm against previous techniques on 2 and 3 dimensional data exposed to a variety of outlier types.

Connected through hyperedge HE-1 (induced by source paper "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "deterministic consensus maximization with biconvex programming"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "alternating direction graph matching"

Connected through hyperedge HE-2 (induced by source paper "least squares registration of point sets over se d using closed form projections"):
  with members:
    - "spectral synchronization of multiple views in se 3"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "a solution for multi alignment by transformation synchronisation"
    - "global registration of multiple point clouds using semidefinite programming"

Connected through hyperedge HE-3 (induced by source paper "globally optimal registration of noisy point clouds"):
  with members:
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "convex relaxations of se 2 and se 3 for visual pose estimation"

Connected through hyperedge HE-4 (induced by source paper "fast and accurate point cloud registration using trees of gaussian mixtures"):
  with members:
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "an adaptive data representation for robust point set registration and merging"
    - "point set registration coherent point drift"

Connected through hyperedge HE-5 (induced by source paper "an effective approach for point clouds registration based on the hard and soft assignments"):
  with members:
    - "rotational projection statistics for 3d local surface description and object recognition"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "effective scaling registration approach by imposing the emphasis on the scale factor"):
  with members:
    - "rotational projection statistics for 3d local surface description and object recognition"
    - "point set registration coherent point drift"

Connected through hyperedge HE-7 (induced by source paper "hidden markov random field iterative closest point"):
  with members:
    - "an adaptive data representation for robust point set registration and merging"

Connected through hyperedge HE-8 (induced by source paper "k means clustering for efficient and robust registration of multi view point sets"):
  with members:
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

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.GR(Graphics)