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: "a geometric framework for stochastic shape analysis"
  Abstract: We introduce a stochastic model of diffeomorphisms, whose action on a variety of data types descends to stochastic evolution of shapes, images and landmarks. The stochasticity is introduced in the vector field which transports the data in the large deformation diffeomorphic metric mapping framework for shape analysis and image registration. The stochasticity thereby models errors or uncertainties of the flow in following the prescribed deformation velocity. The approach is illustrated in the example of finite-dimensional landmark manifolds, whose stochastic evolution is studied both via the Fokker–Planck equation and by numerical simulations. We derive two approaches for inferring parameters of the stochastic model from landmark configurations observed at discrete time points. The first of the two approaches matches moments of the Fokker–Planck equation to sample moments of the data, while the second approach employs an expectation-maximization based algorithm using a Monte Carlo bridge sampling scheme to optimise the data likelihood. We derive and numerically test the ability of the two approaches to infer the spatial correlation length of the underlying noise.

Connected through hyperedge HE-1 (induced by source paper "latent space non linear statistics"):
  with members:
    - "differential geometry and stochastic dynamics with deep learning numerics"
    - "bridge simulation and metric estimation on landmark manifolds"
    - "the riemannian geometry of deep generative models"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-2 (induced by source paper "string methods for stochastic image and shape matching"):
  with members:
    - "differential geometry and stochastic dynamics with deep learning numerics"
    - "a stochastic large deformation model for computational anatomy"

Connected through hyperedge HE-3 (induced by source paper "bridge simulation and metric estimation on landmark manifolds"):
  with members:
    - "computational anatomy in theano"
    - "a stochastic large deformation model for computational anatomy"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-4 (induced by source paper "stochastic metamorphosis with template uncertainties"):
  with members:
    - "the fshape framework for the variability analysis of functional shapes"
    - "the euler poincare theory of metamorphosis"

Connected through hyperedge HE-5 (induced by source paper "computational anatomy in theano"):
  with members:
    - "bridge simulation and metric estimation on landmark manifolds"
    - "a stochastic large deformation model for computational anatomy"

Connected through hyperedge HE-6 (induced by source paper "differential geometry and stochastic dynamics with deep learning numerics"):
  with members:
    - "a stochastic large deformation model for computational anatomy"

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)