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: "multiplex visibility graphs to investigate recurrent neural network dynamics"
  Abstract: A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "deep randomized neural networks"):
  with members:
    - "richness of deep echo state network dynamics"
    - "what s hidden in a randomly weighted neural network"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "deep semi random features for nonlinear function approximation"
    - "stochastic configuration networks fundamentals and algorithms"
    - "deep stochastic configuration networks with universal approximation property"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"

Connected through hyperedge HE-2 (induced by source paper "dynamical anatomy of narma10 benchmark task"):
  with members:
    - "hierarchical composition of memristive networks for real time computing"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-3 (induced by source paper "learning representations for multivariate time series with missing data using temporal kernelized autoencoders"):
  with members:
    - "making the dynamic time warping distance warping invariant"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "structured attention networks"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "mida multiple imputation using denoising autoencoders"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"

Connected through hyperedge HE-4 (induced by source paper "an overview and comparative analysis of recurrent neural networks for short term load forecasting"):
  with members:
    - "lstm a search space odyssey"
    - "temporal overdrive recurrent neural network"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "identifying user habits through data mining on call data records"
    - "wavenet a generative model for raw audio"
    - "architectural complexity measures of recurrent neural networks"
    - "deep residual learning for image recognition"
    - "modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one class classification"

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)