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: "inferring solutions of differential equations using noisy multi fidelity data"
  Abstract: For more than two centuries, solutions of differential equations have been obtained either analytically or numerically based on typically well-behaved forcing and boundary conditions for well-posed problems. We are changing this paradigm in a fundamental way by establishing an interface between probabilistic machine learning and differential equations. We develop data-driven algorithms for general linear equations using Gaussian process priors tailored to the corresponding integro-differential operators. The only observables are scarce noisy multi-fidelity data for the forcing and solution that are not required to reside on the domain boundary. The resulting predictive posterior distributions quantify uncertainty and naturally lead to adaptive solution refinement via active learning. This general framework circumvents the tyranny of numerical discretization as well as the consistency and stability issues of time-integration, and is scalable to high-dimensions.

Connected through hyperedge HE-1 (induced by source paper "symplectic networks intrinsic structure preserving networks for identifying hamiltonian systems"):
  with members:
    - "deeponet learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators"
    - "neural net induced gaussian process regression for function approximation and pde solution"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "machine learning of space fractional differential equations"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "a generalized probabilistic learning approach for multi fidelity uncertainty propagation in complex physical simulations"):
  with members:
    - "fast matrix free evaluation of discontinuous galerkin finite element operators"

Connected through hyperedge HE-3 (induced by source paper "distributed physics informed neural network for data efficient solution to partial differential equations"):
  with members:
    - "physics informed extreme learning machine pielm a rapid method for the numerical solution of partial differential equations"
    - "an introduction to variational autoencoders"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "machine learning of linear differential equations using gaussian processes"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-4 (induced by source paper "neural net induced gaussian process regression for function approximation and pde solution"):
  with members:
    - "on the complexity of learning neural networks"
    - "machine learning of linear differential equations using gaussian processes"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "gaussian processes for big data"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "physics informed extreme learning machine pielm a rapid method for the numerical solution of partial differential equations"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "machine learning of linear differential equations using gaussian processes"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-6 (induced by source paper "decomposing temperature time series with non negative matrix factorization"):
  with members:
    - "machine learning of linear differential equations using gaussian processes"
    - "cryptanalyzing an encryption scheme based on blind source separation"

Connected through hyperedge HE-7 (induced by source paper "hidden physics models machine learning of nonlinear partial differential equations"):
  with members:
    - "machine learning of linear differential equations using gaussian processes"
    - "deep multi fidelity gaussian processes"
    - "gaussian processes for big data"
    - "exploiting compositionality to explore a large space of model structures"

Connected through hyperedge HE-8 (induced by source paper "machine learning of space fractional differential equations"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "machine learning of linear differential equations using gaussian processes"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "gaussian processes for big data"

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.LG(Machine Learning)