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: "deep learning for physical processes incorporating prior scientific knowledge"
  Abstract: We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or physics. However, despite considerable successes in a variety of application domains, the machine learning field is not yet ready to handle the level of complexity required by such problems. Using an example application, namely Sea Surface Temperature Prediction, we show how general background knowledge gained from physics could be used as a guideline for designing efficient Deep Learning models. In order to motivate the approach and to assess its generality we demonstrate a formal link between the solution of a class of differential equations underlying a large family of physical phenomena and the proposed model. Experiments and comparison with series of baselines including a state of the art numerical approach is then provided.

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 "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"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "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-3 (induced by source paper "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "extracting structured dynamical systems using sparse optimization with very few samples"
    - "stable architectures for deep neural networks"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"

Connected through hyperedge HE-4 (induced by source paper "learning programmatically structured representations with perceptor gradients"):
  with members:
    - "towards symbolic reinforcement learning with common sense"
    - "isolating sources of disentanglement in variational autoencoders"
    - "programmatically interpretable reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "learning to infer graphics programs from hand drawn images"
    - "using program induction to interpret transition system dynamics"
    - "beating atari with natural language guided reinforcement learning"
    - "differentiable programs with neural libraries"

Connected through hyperedge HE-5 (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"
    - "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"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "forward stability of resnet and its variants"
    - "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 "rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling"):
  with members:
    - "learning a physical long term predictor"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "arrhythmia classification from the abductive interpretation of short single lead ecg records"
    - "convolutional recurrent neural networks for electrocardiogram classification"
    - "on loss functions for deep neural networks in classification"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-7 (induced by source paper "learning dynamical systems from partial observations"):
  with members:
    - "neural ordinary differential equations"
    - "machine learning of linear differential equations using gaussian processes"
    - "bilinear residual neural network for the identification and forecasting of dynamical systems"
    - "video pixel networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-8 (induced by source paper "combining physics based domain knowledge and machine learning using variational gaussian processes with explicit linear prior"):
  with members:
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "variational gaussian process state space models"
    - "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.AI(Artificial Intelligence)