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: "robust inversion dimensionality reduction and randomized sampling"
  Abstract: We consider a class of inverse problems in which the forward model is the solution operator to linear ODEs or PDEs. This class admits several dimensionality-reduction techniques based on data averaging or sampling, which are especially useful for large-scale problems. We survey these approaches and their connection to stochastic optimization. The data-averaging approach is only viable, however, for a least-squares misfit, which is sensitive to outliers in the data and artifacts unexplained by the forward model. This motivates us to propose a robust formulation based on the Student’s t-distribution of the error. We demonstrate how the corresponding penalty function, together with the sampling approach, can obtain good results for a large-scale seismic inverse problem with 50 % corrupted data.

Connected through hyperedge HE-1 (induced by source paper "a unified 2d 3d large scale software environment for nonlinear inverse problems"):
  with members:
    - "julia a fresh approach to numerical computing"
    - "a parallel sweeping preconditioner for heterogeneous 3d helmholtz equations"
    - "automated derivation of the adjoint of high level transient finite element programs"

Connected through hyperedge HE-2 (induced by source paper "a data scalable randomized misfit approach for solving large scale pde constrained inverse problems"):
  with members:
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "data completion and stochastic algorithms for pde inversion problems with many measurements"

Connected through hyperedge HE-3 (induced by source paper "approximating the spectral sums of large scale matrices using chebyshev approximations"):
  with members:
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "efficient estimation of eigenvalue counts in an interval"

Connected through hyperedge HE-4 (induced by source paper "stop wasting my gradients practical svrg"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "hybrid deterministic stochastic methods for data fitting"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-5 (induced by source paper "large scale log determinant computation through stochastic chebyshev expansions"):
  with members:
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "efficient estimation of eigenvalue counts in an interval"
    - "efficient exact inference in planar ising models"

Connected through hyperedge HE-6 (induced by source paper "accelerating hessian free optimization for deep neural networks by implicit preconditioning and sampling"):
  with members:
    - "hybrid deterministic stochastic methods for data fitting"

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.CE(Computational Engineering, Finance, and Science)