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: "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
  Abstract: We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of partial differential equations. Depending on whether the available data is scattered in space-time or arranged in fixed temporal snapshots, we introduce two main classes of algorithms, namely continuous time and discrete time models. The effectiveness of our approach is demonstrated using a wide range of benchmark problems in mathematical physics, including conservation laws, incompressible fluid flow, and the propagation of nonlinear shallow-water waves.

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"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "implicit geometric regularization for learning shapes"):
  with members:
    - "controlling neural level sets"
    - "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "learning elementary structures for 3d shape generation and matching"
    - "bsp net generating compact meshes via binary space partitioning"
    - "voronoinet general functional approximators with local support"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-3 (induced by source paper "incorporating symmetry into deep dynamics models for improved generalization"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "invariant representations through adversarial forgetting"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "equivariant neural networks and equivarification"
    - "invariant representations without adversarial training"
    - "neural ordinary differential equations"
    - "learning steerable filters for rotation equivariant cnns"

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"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "comparing recurrent and convolutional neural networks for predicting wave propagation"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning a physical long term predictor"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "flownet learning optical flow with convolutional networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "machine learning in cardiovascular flows modeling predicting arterial blood pressure from non invasive 4d flow mri data using physics informed neural networks"):
  with members:
    - "machine learning of linear differential equations using gaussian processes"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "explainable machine learning for scientific insights and discoveries"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "a rate distortion framework for explaining neural network decisions"
    - "explaining explanations in ai"
    - "neural guided symbolic regression with semantic prior"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "causality for machine learning"
    - "interpretations are useful penalizing explanations to align neural networks with prior knowledge"
    - "the challenge of crafting intelligible intelligence"

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)