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: "difference between memory and prediction in linear recurrent networks"
  Abstract: Recurrent networks are trained to memorize their input better, often in the hopes that such training will increase the ability of the network to predict. We show that networks designed to memorize input can be arbitrarily bad at prediction. We also find, for several types of inputs, that one-node networks optimized for prediction are nearly at upper bounds on predictive capacity given by Wiener filters, and are roughly equivalent in performance to randomly generated five-node networks. Our results suggest that maximizing memory capacity leads to very different networks than maximizing predictive capacity, and that optimizing recurrent weights can decrease reservoir size by half an order of magnitude.

Connected through hyperedge HE-1 (induced by source paper "the power of linear recurrent neural networks"):
  with members:
    - "analysing soccer games with clustering and conceptors"
    - "a critical review of recurrent neural networks for sequence learning"
    - "training recurrent networks online without backtracking"
    - "controlling recurrent neural networks by conceptors"
    - "learning input and recurrent weight matrices in echo state networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "risk bounds for reservoir computing"):
  with members:
    - "differentiable reservoir computing"
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "theory and algorithms for forecasting time series"
    - "spectrally normalized margin bounds for neural networks"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"

Connected through hyperedge HE-3 (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"
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "reservoir computing on the hypersphere"

Connected through hyperedge HE-4 (induced by source paper "coupled nonlinear delay systems as deep convolutional neural networks"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "all optical machine learning using diffractive deep neural networks"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "embodiment of learning in electro optical signal processors"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-5 (induced by source paper "network structure effects in reservoir computers"):
  with members:
    - "computing optimal discrete readout weights in reservoir computing is np hard"
    - "rapid time series prediction with a hardware based reservoir computer"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"

Connected through hyperedge HE-6 (induced by source paper "predictive neural networks"):
  with members:
    - "analysing soccer games with clustering and conceptors"
    - "robocupsimdata a robocup soccer research dataset"
    - "a critical review of recurrent neural networks for sequence learning"
    - "training recurrent networks online without backtracking"
    - "controlling recurrent neural networks by conceptors"

Connected through hyperedge HE-7 (induced by source paper "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"):
  with members:
    - "brain inspired photonic signal processor for generating periodic patterns and emulating chaotic systems"
    - "optoelectronic reservoir computing"

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)