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 stochastic large deformation model for computational anatomy"
  Abstract: In the study of shapes of human organs using computational anatomy, variations are found to arise from inter-subject anatomical differences, disease-specific effects, and measurement noise. This paper introduces a stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. By accounting for randomness in a particular setup which is crafted to fit the geometrical properties of LDDMM, we formulate the template estimation problem for landmarks with noise and give two methods for efficiently estimating the parameters of the noise fields from a prescribed data set. One method directly approximates the time evolution of the variance of each landmark by a finite set of differential equations, and the other is based on an Expectation-Maximisation algorithm. In the second method, the evaluation of the data likelihood is achieved without registering the landmarks, by applying bridge sampling using a stochastically perturbed version of the large deformation gradient flow algorithm. The method and the estimation algorithms are experimentally validated on synthetic examples and shape data of human corpora callosa.

Connected through hyperedge HE-1 (induced by source paper "a geometric framework for stochastic shape analysis"):
  with members:
    - "bridge simulation and metric estimation on landmark manifolds"
    - "most likely separation of intensity and warping effects in image registration"
    - "theano a python framework for fast computation of mathematical expressions"
    - "higher order spatial accuracy in diffeomorphic image registration"

Connected through hyperedge HE-2 (induced by source paper "string methods for stochastic image and shape matching"):
  with members:
    - "a geometric framework for stochastic shape analysis"
    - "differential geometry and stochastic dynamics with deep learning numerics"

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

Connected through hyperedge HE-4 (induced by source paper "computational anatomy in theano"):
  with members:
    - "a geometric framework for stochastic shape analysis"
    - "bridge simulation and metric estimation on landmark manifolds"

Connected through hyperedge HE-5 (induced by source paper "differential geometry and stochastic dynamics with deep learning numerics"):
  with members:
    - "a geometric framework for stochastic shape analysis"

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)