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: "the euler poincare theory of metamorphosis"
  Abstract: In the pattern matching approach to imaging science, the process of ``metamorphosis'' is template matching with dynamical templates. Here, we recast the metamorphosis equations of into the Euler-Poincare variational framework of and show that the metamorphosis equations contain the equations for a perfect complex fluid \cite{Ho2002}. This result connects the ideas underlying the process of metamorphosis in image matching to the physical concept of order parameter in the theory of complex fluids. After developing the general theory, we reinterpret various examples, including point set, image and density metamorphosis. We finally discuss the issue of matching measures with metamorphosis, for which we provide existence theorems for the initial and boundary value problems.

Connected through hyperedge HE-1 (induced by source paper "geometrization of deep networks for the interpretability of deep learning systems"):
  with members:
    - "adversarial noise layer regularize neural network by adding noise"
    - "relational inductive biases deep learning and graph networks"
    - "learning to reweight examples for robust deep learning"
    - "neural ordinary differential equations"
    - "how deep learning works the geometry of deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"

Connected through hyperedge HE-2 (induced by source paper "how deep learning works the geometry of deep learning"):
  with members:
    - "identity mappings in deep residual networks"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "a critical review of recurrent neural networks for sequence learning"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "reinforcement learning neural turing machines revised"
    - "neural turing machines"

Connected through hyperedge HE-3 (induced by source paper "stochastic metamorphosis with template uncertainties"):
  with members:
    - "a geometric framework for stochastic shape analysis"
    - "the fshape framework for the variability analysis of functional shapes"

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)