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: "synthesis of recurrent neural networks for dynamical system simulation"
  Abstract: We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector field representation of a given dynamical system using backpropagation, then recast, using matrix manipulations, as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time.

Connected through hyperedge HE-1 (induced by source paper "inferring dynamical systems with long range dependencies through line attractor regularization"):
  with members:
    - "learning dynamical systems from partial observations"
    - "gated recurrent units viewed through the lens of continuous time dynamical systems"
    - "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "state space lstm models with particle mcmc inference"
    - "gated orthogonal recurrent units on learning to forget"
    - "recurrent orthogonal networks and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-2 (induced by source paper "accurate robotic pouring for serving drinks"):
  with members:
    - "accurate pouring with an autonomous robot using an rgb d camera"
    - "functional object oriented network construction expansion"
    - "a dataset of daily interactive manipulation"
    - "learning to pour"
    - "functional object oriented network for manipulation learning"
    - "visual closed loop control for pouring liquids"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"):
  with members:
    - "a recurrent latent variable model for sequential data"
    - "improving performance of recurrent neural network with relu nonlinearity"
    - "review fight sample degeneracy and impoverishment in particle filters a review of intelligent approaches"

Connected through hyperedge HE-4 (induced by source paper "learning to pour"):
  with members:
    - "functional object oriented network for manipulation learning"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "recurrent network models for human dynamics"
    - "sequence to sequence video to text"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "learning to execute"

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.NE(Neural and Evolutionary Computing)