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: "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
  Abstract: A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version ...

Connected through hyperedge HE-1 (induced by source paper "combining machine learning with knowledge based modeling for scalable forecasting and subgrid scale closure of large complex spatiotemporal systems"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "efficient design of hardware enabled reservoir computing in fpgas"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "atomnet a deep convolutional neural network for bioactivity prediction in structure based drug discovery"

Connected through hyperedge HE-2 (induced by source paper "learning ergodic averages in chaotic systems"):
  with members:
    - "data assimilation in a nonlinear time delayed dynamical system with lagrangian optimization"
    - "end to end learning for self driving cars"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-3 (induced by source paper "artificial intelligence chaos prediction and understanding in science"):
  with members:
    - "enforcing constraints for time series prediction in supervised unsupervised and reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "echo state networks with self normalizing activations on the hyper sphere"):
  with members:
    - "interpreting recurrent neural networks behaviour via excitable network attractors"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "echo state networks are universal"
    - "chasing the echo state property"
    - "a characterization of the edge of criticality in binary echo state networks"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "reservoir computing on the hypersphere"
    - "difference between memory and prediction in linear recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "data driven prediction of a multi scale lorenz 96 chaotic system using deep learning methods reservoir computing ann and rnn lstm"):
  with members:
    - "long term forecasting using higher order tensor rnns"
    - "doing moore with less leapfrogging moore s law with inexactness for supercomputing"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a non intrusive reduced order modeling framework for quasi geostrophic turbulence"):
  with members:
    - "short note on the behavior of recurrent neural network for noisy dynamical system"
    - "dr rnn a deep residual recurrent neural network for model reduction"
    - "deep learning for time series analysis"
    - "efficient construction of local parametric reduced order models using machine learning techniques"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "model free prediction of spatiotemporal dynamical systems with recurrent neural networks role of network spectral radius"):
  with members:
    - "reservoir computing with a single time delay autonomous boolean node"

Connected through hyperedge HE-8 (induced by source paper "rapid time series prediction with a hardware based reservoir computer"):
  with members:
    - "reservoir computing with a single time delay autonomous boolean node"

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)