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: "conservative model reduction for finite volume models"
  Abstract: Abstract   This work proposes a method for model reduction of finite-volume models that guarantees the resulting reduced-order model is conservative, thereby preserving the structure intrinsic to finite-volume discretizations. The proposed reduced-order models associate with optimization problems characterized by a minimum-residual objective function and nonlinear equality constraints that explicitly enforce conservation over subdomains. Conservative Galerkin projection arises from formulating this optimization problem at the time-continuous level, while conservative least-squares Petrov–Galerkin (LSPG) projection associates with a time-discrete formulation. We equip these approaches with hyper-reduction techniques in the case of nonlinear flux and source terms, and also provide approaches for handling infeasibility. In addition, we perform analyses that include deriving conditions under which conservative Galerkin and conservative LSPG are equivalent, as well as deriving a posteriori error bounds. Numerical experiments performed on a parameterized quasi-1D Euler equation demonstrate the ability of the proposed method to ensure not only global conservation, but also significantly lower state-space errors than nonconservative reduced-order models such as standard Galerkin and LSPG projection.

Connected through hyperedge HE-1 (induced by source paper "pressio enabling projection based model reduction for large scale nonlinear dynamical systems"):
  with members:
    - "stability conditions for the explicit integration of projection based nonlinear reduced order and hyper reduced structural mechanics finite element models"
    - "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"
    - "space time least squares petrov galerkin projection for nonlinear model reduction"
    - "pymor generic algorithms and interfaces for model order reduction"
    - "preserving lagrangian structure in nonlinear model reduction with application to structural dynamics"
    - "occa a unified approach to multi threading languages"

Connected through hyperedge HE-2 (induced by source paper "regression based sparse polynomial chaos for uncertainty quantification of subsurface flow models"):
  with members:
    - "a machine learning approach for efficient uncertainty quantification using multiscale methods"

Connected through hyperedge HE-3 (induced by source paper "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"):
  with members:
    - "deep dynamical modeling and control of unsteady fluid flows"
    - "learning koopman invariant subspaces for dynamic mode decomposition"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "adaptive h refinement for reduced order models"

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.NA(Numerical Analysis)