human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "integer echo state networks hyperdimensional reservoir computing"):
  Abstract: We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed Integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The intESN architecture is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such an architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss.

Includes members:
  Member "reservoir computing approaches for representation and classification of multivariate time series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for time series classification a review")
  Member "a theory of sequence indexing and working memory in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperembed tradeoffs between resources and performance in nlp tasks with hyperdimensional computing enabled embedding of n gram statistics")
  Member "reservoir computing and extreme learning machines using pairs of cellular automata rules":
    also connected through other hyperedges:
      (none)
  Member "deep reservoir computing using cellular automata":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in physical reservoir computing a review")
  Member "quantized neural networks training neural networks with low precision weights and activations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "switchable precision neural networks")
  Member "representing objects relations and sequences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "density encoding enables resource efficient randomly connected neural networks")
  Member "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recursive sketches for modular deep learning")

Question: Please tell me which class the source paper 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)