human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised machine learning based on non negative tensor factorization for analyzing reactive mixing"):
  Abstract: Abstract   Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTF k ) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on  k -means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction    A  +  B  →  C   , where species  A  and  B  react to form species  C . The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product  C . The presented ML analysis allowed us to identify additive temporal and spatial features that characterize mixing behavior. The application of the proposed NTF k  approach is not limited to reactive-mixing. NTF k  can be readily applied to any observed or simulated datasets that can be represented as tensors (multi-dimensional arrays) and have separable latent signatures or features.

Includes members:
  Member "identification of release sources in advection diffusion system by machine learning combined with green s function inverse method":
    also connected through other hyperedges:
      (none)
  Member "large scale optimization based non negative computational framework for diffusion equations parallel implementation and performance studies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "composable block solvers for the four field double porosity permeability model")
  Member "nonnegative matrix factorization for identification of unknown number of sources emitting delayed signals":
    also connected through other hyperedges:
      (none)
  Member "parallel tensor compression for large scale scientific data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "on mesh restrictions to satisfy comparison principles maximum principles and the non negative constraint recent developments and new results":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a scalable variational inequality approach for flow through porous media models with pressure dependent viscosity")
  Member "a numerical framework for diffusion controlled bimolecular reactive systems to enforce maximum principles and the non negative constraint":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a scalable variational inequality approach for flow through porous media models with pressure dependent viscosity")
  Member "julia a fast dynamic language for technical computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentiate everything with a reversible programming language")

Question: Please tell me which class the source paper 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.CE(Computational Engineering, Finance, and Science)