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 guided recurrent neural networks for modeling dynamical systems application to monitoring water temperature and quality in lakes"
  Abstract: In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems. We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model. Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks. We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimension and time dimension. By using scientific knowledge to guide the construction and learning the data-driven model, we demonstrate that this method can achieve better prediction accuracy as well as scientific consistency of results.

Connected through hyperedge HE-1 (induced by source paper "accelerating neural odes with spectral elements"):
  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"
    - "neural ordinary differential equations"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "adaptive computation time for recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "snode spectral discretization of neural odes for system identification"):
  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"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "recurrent highway networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "enhancing streamflow forecast and extracting insights using long short term memory networks with data integration at continental scales"):
  with members:
    - "neuralhydrology interpreting lstms in hydrology"
    - "lstm a search space odyssey"
    - "deep learning in neural networks"
    - "adadelta an adaptive learning rate method"

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)