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: "on mesh restrictions to satisfy comparison principles maximum principles and the non negative constraint recent developments and new results"
  Abstract: This paper concerns with mesh restrictions that are needed to satisfy several important mathematical properties -- maximum principles, comparison principles, and the non-negative constraint -- for a general linear second-order elliptic partial differential equation. We critically review some recent developments in the field of discrete maximum principles, derive new results, and discuss some possible future research directions in this area. In particular, we derive restrictions for a three-node triangular (T3) element and a four-node quadrilateral (Q4) element to satisfy comparison principles, maximum principles, and the non-negative constraint under the standard single-field Galerkin formulation. Analysis is restricted to uniformly elliptic linear differential operators in divergence form with Dirichlet boundary conditions specified on the entire boundary of the domain. Various versions of maximum principles and comparison principles are discussed in both continuous and discrete settings. In the literature, it is well-known that an acute-angled triangle is sufficient to satisfy the discrete weak maximum principle for pure isotropic diffusion. An iterative algorithm is developed to construct simplicial meshes that preserves discrete maximum principles using existing open source mesh generators. Various numerical examples based on different types of triangulations are presented to show the pros and cons of placing restrictions on a computational mesh. We also quantify local and global mass conservation errors using representative numerical examples, and illustrate the performance of metric-based meshes with respect to mass conservation.

Connected through hyperedge HE-1 (induced by source paper "unsupervised machine learning based on non negative tensor factorization for analyzing reactive mixing"):
  with members:
    - "identification of release sources in advection diffusion system by machine learning combined with green s function inverse method"
    - "large scale optimization based non negative computational framework for diffusion equations parallel implementation and performance studies"
    - "nonnegative matrix factorization for identification of unknown number of sources emitting delayed signals"
    - "parallel tensor compression for large scale scientific data"
    - "a numerical framework for diffusion controlled bimolecular reactive systems to enforce maximum principles and the non negative constraint"
    - "julia a fast dynamic language for technical computing"

Connected through hyperedge HE-2 (induced by source paper "a scalable variational inequality approach for flow through porous media models with pressure dependent viscosity"):
  with members:
    - "a performance spectrum for parallel computational frameworks that solve pdes"
    - "firedrake automating the finite element method by composing abstractions"
    - "large scale optimization based non negative computational framework for diffusion equations parallel implementation and performance studies"
    - "an algorithm for the optimization of finite element integration loops"
    - "variational inequality approach to enforcing the non negative constraint for advection diffusion equations"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "a numerical framework for diffusion controlled bimolecular reactive systems to enforce maximum principles and the non negative constraint"
    - "a numerical methodology for enforcing maximum principles and the non negative constraint for transient diffusion equations"

Connected through hyperedge HE-3 (induced by source paper "variational inequality approach to enforcing the non negative constraint for advection diffusion equations"):
  with members:
    - "firedrake automating the finite element method by composing abstractions"
    - "large scale optimization based non negative computational framework for diffusion equations parallel implementation and performance studies"
    - "an algorithm for the optimization of finite element integration loops"
    - "a parallel edge orientation algorithm for quadrilateral meshes"
    - "a structure exploiting numbering algorithm for finite elements on extruded meshes and its performance evaluation in firedrake"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "a numerical framework for diffusion controlled bimolecular reactive systems to enforce maximum principles and the non negative constraint"
    - "a numerical methodology for enforcing maximum principles and the non negative constraint for transient diffusion equations"

Connected through hyperedge HE-4 (induced by source paper "large scale optimization based non negative computational framework for diffusion equations parallel implementation and performance studies"):
  with members:
    - "flexible scalable mesh and data management using petsc dmplex"
    - "a numerical framework for diffusion controlled bimolecular reactive systems to enforce maximum principles and the non negative constraint"
    - "achieving high performance with unified residual evaluation"
    - "a numerical methodology for enforcing maximum principles and the non negative constraint for transient diffusion equations"
    - "enforcing the non negativity constraint and maximum principles for diffusion with decay on general computational grids"
    - "non negative mixed finite element formulations for a tensorial diffusion equation"
    - "mesh algorithms for pde with sieve i mesh distribution"

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)