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: "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
  Abstract: Abstract   Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data-driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE-Net 2.0, to discover (time-dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE-Net 2.0 is based on our earlier work  [1]  where the original version of PDE-Net was proposed. PDE-Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi-layer neural network for model recovery. Comparing with existing approaches, PDE-Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE-Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.

Connected through hyperedge HE-1 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  with members:
    - "differentiable physics informed graph networks"
    - "improved conditional vrnns for video prediction"
    - "neural jump stochastic differential equations"
    - "unsupervised learning of object structure and dynamics from videos"
    - "compositional video prediction"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "using deep learning to extend the range of air pollution monitoring and forecasting"):
  with members:
    - "fd net with auxiliary time steps fast prediction of pdes using hessian free trust region methods"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "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 "time a transparent interpretable model adaptive and explainable neural network for dynamic physical processes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpretable convolutional neural networks"
    - "the mythos of model interpretability"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (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"
    - "data driven discovery of free form governing differential equations"
    - "hidden physics models machine learning of nonlinear partial 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-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"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "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 "finitenet a fully convolutional lstm network architecture for time dependent partial differential equations"):
  with members:
    - "learning neural pde solvers with convergence guarantees"
    - "solving differential equations with unknown constitutive relations as recurrent neural networks"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "mgnet a unified framework of multigrid and convolutional neural network"):
  with members:
    - "learning neural pde solvers with convergence guarantees"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "universality of deep convolutional neural networks"
    - "a flow model of neural networks"
    - "learning across scales a multiscale method for convolution neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "dual path networks"

Connected through hyperedge HE-8 (induced by source paper "neupde neural network based ordinary and partial differential equations for modeling time dependent data"):
  with members:
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "forward stability of resnet and its variants"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "machine learning of linear differential equations using gaussian processes"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

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)