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: "machine learning augmented predictive modeling of turbulent separated flows over airfoils"
  Abstract: A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.

Connected through hyperedge HE-1 (induced by source paper "deep learning for determining a near optimal topological design without any iteration"):
  with members:
    - "neural networks for topology optimization"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "tacotron towards end to end speech synthesis"
    - "interactive 3d modeling with a generative adversarial network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accelerating eulerian fluid simulation with convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "learning neural pde solvers with convergence guarantees"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "the case for learned index structures"
    - "deep learning the physics of transport phenomena"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"
    - "learning to learn by gradient descent by gradient descent"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "hybridnet integrating model based and data driven learning to predict evolution of dynamical systems"):
  with members:
    - "physics guided neural networks pgnn an application in lake temperature modeling"
    - "unsupervised learning for physical interaction through video prediction"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "generating videos with scene dynamics"
    - "unsupervised learning of video representations using lstms"
    - "action conditional video prediction using deep networks in atari games"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-4 (induced by source paper "theory guided data science a new paradigm for scientific discovery from data"):
  with members:
    - "a contextual bandit approach to personalized news article recommendation"

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.CE(Computational Engineering, Finance, and Science)