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: "global registration of multiple point clouds using semidefinite programming"
  Abstract: Consider $N$ points in $\mathbb{R}^d$ and $M$ local coordinate systems that are related through unknown rigid transforms. For each point we are given (possibly noisy) measurements of its local coordinates in some of the coordinate systems. Alternatively, for each coordinate system, we observe the coordinates of a subset of the points. The problem of estimating the global coordinates of the $N$ points (up to a rigid transform) from such measurements comes up in distributed approaches to molecular conformation and sensor network localization, and also in computer vision and graphics. #R##N#The least-squares formulation of this problem, though non-convex, has a well known closed-form solution when $M=2$ (based on the singular value decomposition). However, no closed form solution is known for $M\geq 3$. #R##N#In this paper, we demonstrate how the least-squares formulation can be relaxed into a convex program, namely a semidefinite program (SDP). By setting up connections between the uniqueness of this SDP and results from rigidity theory, we prove conditions for exact and stable recovery for the SDP relaxation. In particular, we prove that the SDP relaxation can guarantee recovery under more adversarial conditions compared to earlier proposed spectral relaxations, and derive error bounds for the registration error incurred by the SDP relaxation. #R##N#We also present results of numerical experiments on simulated data to confirm the theoretical findings. We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the semidefinite program (i.e., we are able to solve the original non-convex least-squares problem) up to a certain noise threshold, and (b) the semidefinite program performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels.

Connected through hyperedge HE-1 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"

Connected through hyperedge HE-2 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations"):
  with members:
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "distributable consistent multi object matching"
    - "a constrained clustering based approach for matching a collection of feature sets"
    - "spectral synchronization of multiple views in se 3"
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "alternating direction graph matching"

Connected through hyperedge HE-3 (induced by source paper "multi frequency phase synchronization"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "information recovery from pairwise measurements"
    - "approximating the little grothendieck problem over the orthogonal and unitary groups"

Connected through hyperedge HE-4 (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"
    - "outlier robust icp for minimizing fractional rmsd"

Connected through hyperedge HE-5 (induced by source paper "on uniquely registrable networks"):
  with members:
    - "large scale sensor network localization via rigid subnetwork registration"
    - "distributed maximum likelihood sensor network localization"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "on affine rigidity"

Connected through hyperedge HE-6 (induced by source paper "robust rotation synchronization via low rank and sparse matrix decomposition"):
  with members:
    - "a survey of structure from motion"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "spectral synchronization of multiple views in se 3"
    - "on computing the translations norm in the epipolar graph"
    - "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"
    - "a solution for multi alignment by transformation synchronisation"
    - "cramer rao bounds for synchronization of rotations"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-7 (induced by source paper "non iterative rigid 2d 3d point set registration using semidefinite programming"):
  with members:
    - "near optimal joint object matching via convex relaxation"
    - "exact and stable recovery of rotations for robust synchronization"
    - "point set registration coherent point drift"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "large scale sensor network localization via rigid subnetwork registration"):
  with members:
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"

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)