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 "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  Abstract: We present an entirely new geometric and probabilistic approach to synchronization of correspondences across multiple sets of objects or images. In particular, we present two algorithms: (1) Birkhoff-Riemannian L-BFGS for optimizing the relaxed version of the combinatorially intractable cycle consistency loss in a principled manner, (2) Birkhoff-Riemannian Langevin Monte Carlo for generating samples on the Birkhoff Polytope and estimating the confidence of the found solutions. To this end, we first introduce the very recently developed Riemannian geometry of the Birkhoff Polytope. Next, we introduce a new probabilistic synchronization model in the form of a Markov Random Field (MRF). Finally, based on the first order retraction operators, we formulate our problem as simulating a stochastic differential equation and devise new integrators. We show on both synthetic and real datasets that we achieve high quality multi-graph matching results with faster convergence and reliable confidence/uncertainty estimates.

Includes members:
  Member "synchronisation of partial multi matchings via non negative factorisations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clear a consistent lifting embedding and alignment rectification algorithm for multi agent data association")
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial joint image and pose distribution learning for camera pose regression and refinement")
  Member "averaging stochastic gradient descent on riemannian manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive stochastic gradient algorithms on riemannian manifolds")
  Member "orb slam2 an open source slam system for monocular stereo and rgb d cameras":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "cad priors for accurate and flexible instance reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning multiview 3d point cloud registration")
  Member "consistent multiple graph matching with multi layer random walks synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "higher order projected power iterations for scalable multi matching")
  Member "multi image semantic matching by mining consistent features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks")

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)