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: "hidden physics models machine learning of nonlinear partial differential equations"
  Abstract: Abstract   While there is currently a lot of enthusiasm about “big data”, useful data is usually “small” and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier–Stokes, Schrodinger, Kuramoto–Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

Connected through hyperedge HE-1 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition"):
  with members:
    - "understanding and mitigating gradient pathologies in physics informed neural networks"
    - "variational physics informed neural networks for solving partial differential equations"
    - "nonlinear approximation and deep relu networks"
    - "varnet variational neural networks for the solution of partial differential equations"
    - "function approximation by deep networks"
    - "d3m a deep domain decomposition method for partial differential equations"
    - "locally adaptive activation functions with slope recovery term for deep and physics informed neural networks"
    - "deepxde a deep learning library for solving differential equations"

Connected through hyperedge HE-2 (induced by source paper "using deep learning to extend the range of air pollution monitoring and forecasting"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "fd net with auxiliary time steps fast prediction of pdes using hessian free trust region methods"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "deep air learning interpolation prediction and feature analysis of fine grained air quality"
    - "machine learning of linear differential equations using gaussian processes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "computational model discovery with reinforcement learning"):
  with members:
    - "coarse scale pdes from fine scale observations via machine learning"
    - "deep learning for symbolic mathematics"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "data driven discovery of free form governing differential equations"
    - "learning dexterous in hand manipulation"
    - "neural ordinary differential equations"
    - "automatic derivation of formulas using reforcement learning"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

Connected through hyperedge HE-4 (induced by source paper "a derivative free method for solving elliptic partial differential equations with deep neural networks"):
  with members:
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "learning across scales a multiscale method for convolution neural networks"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "highway and residual networks learn unrolled iterative estimation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "on generalized residue network for deep learning of unknown dynamical systems"):
  with members:
    - "em like learning chaotic dynamics from noisy and partial observations"
    - "neupde neural network based ordinary and partial differential equations for modeling time dependent data"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a machine learning approach for efficient uncertainty quantification using multiscale methods"
    - "machine learning of linear differential equations using gaussian processes"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

Connected through hyperedge HE-6 (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"
    - "machine learning of space fractional differential equations"
    - "inferring solutions of differential equations using noisy multi fidelity data"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-8 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

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.AI(Artificial Intelligence)