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: "deep reservoir computing using cellular automata"
  Abstract: Recurrent Neural Networks (RNNs) have been a prominent concept within artificial intelligence. They are inspired by Biological Neural Networks (BNNs) and provide an intuitive and abstract representation of how BNNs work. Derived from the more generic Artificial Neural Networks (ANNs), the recurrent ones are meant to be used for temporal tasks, such as speech recognition, because they are capable of memorizing historic input. However, such networks are very time consuming to train as a result of their inherent nature. Recently, Echo State Networks and Liquid State Machines have been proposed as possible RNN alternatives, under the name of Reservoir Computing (RC). RCs are far more easy to train. In this paper, Cellular Automata are used as reservoir, and are tested on the 5-bit memory task (a well known benchmark within the RC community). The work herein provides a method of mapping binary inputs from the task onto the automata, and a recurrent architecture for handling the sequential aspects of it. Furthermore, a layered (deep) reservoir architecture is proposed. Performances are compared towards earlier work, in addition to its single-layer version. Results show that the single CA reservoir system yields similar results to state-of-the-art work. The system comprised of two layered reservoirs do show a noticeable improvement compared to a single CA reservoir. This indicates potential for further research and provides valuable insight on how to design CA reservoir systems.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deepteginn deep learning based tools to extract graphs from images of neural networks"):
  with members:
    - "yolov3 an incremental improvement"
    - "w net a deep model for fully unsupervised image segmentation"
    - "a neural algorithm of artistic style"
    - "scikit image image processing in python"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "cellular memristive output reservoir cmor"):
  with members:
    - "recent advances in physical reservoir computing a review"

Connected through hyperedge HE-4 (induced by source paper "holographic automata for ambient immersive a i via reservoir computing"):
  with members:
    - "reservoir computing using non uniform binary cellular automata"
    - "cross boundary behavioural reprogrammability of cellular automata from emulation networks"
    - "connectionist symbolic machine intelligence using cellular automata based reservoir hyperdimensional computing"
    - "conceptors an easy introduction"
    - "controlling recurrent neural networks by conceptors"
    - "on geometric algebra representation of binary spatter codes"
    - "distributed computation as hierarchy"

Connected through hyperedge HE-5 (induced by source paper "reservoir computing hardware with cellular automata"):
  with members:
    - "binarized convolutional neural networks with separable filters for efficient hardware acceleration"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "connectionist symbolic machine intelligence using cellular automata based reservoir hyperdimensional computing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "integer echo state networks hyperdimensional reservoir computing"):
  with members:
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "reservoir computing and extreme learning machines using pairs of cellular automata rules"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "representing objects relations and sequences"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

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)