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: "brain inspired photonic signal processor for generating periodic patterns and emulating chaotic systems"
  Abstract: Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals. Its hardware implementations have received much attention because of their simplicity and remarkable performance on a series of benchmark tasks. In previous experiments the output was uncoupled from the system and in most cases simply computed offline on a post-processing computer. However, numerical investigations have shown that feeding the output back into the reservoir would open the possibility of long-horizon time series forecasting. Here we present a photonic reservoir computer with output feedback, and demonstrate its capacity to generate periodic time series and to emulate chaotic systems. We study in detail the effect of experimental noise on system performance. In the case of chaotic systems, this leads us to introduce several metrics, based on standard signal processing techniques, to evaluate the quality of the emulation. Our work significantly enlarges the range of tasks that can be solved by hardware reservoir computers, and therefore the range of applications they could potentially tackle. It also raises novel questions in nonlinear dynamics and chaos theory.

Connected through hyperedge HE-1 (induced by source paper "physics for neuromorphic computing"):
  with members:
    - "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling"
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "energy and policy considerations for deep learning in nlp"
    - "neuromorphic computing with multi memristive synapses"
    - "all optical machine learning using diffractive deep neural networks"
    - "biological plausibility and stochasticity in scalable vo2 active memristor neurons"
    - "computing with networks of nonlinear mechanical oscillators"

Connected through hyperedge HE-2 (induced by source paper "recent advances in physical reservoir computing a review"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "echo state networks are universal"
    - "reservoir computing hardware with cellular automata"
    - "scaling up echo state networks with multiple light scattering"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "reservoir computing universality with stochastic inputs"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "reservoir computing using non uniform binary cellular automata"

Connected through hyperedge HE-3 (induced by source paper "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"):
  with members:
    - "difference between memory and prediction in linear recurrent networks"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-4 (induced by source paper "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"):
  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.NE(Neural and Evolutionary Computing)