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 "a theory of sequence indexing and working memory in recurrent neural networks"):
  Abstract: To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA), and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy and crosstalk noise limits the retrieval accuracy and information capacity. A novel theory to optimize memory performance in such networks is presented and compared with simulation experiments. The theory describes linear readout of analog data, and readout with winner-take-all error correction of symbolic data as proposed in VSA models. We find that diverse VSA models from the literature have universal performance properties, which are superior to what previous analyses predicted. Further, we propose novel VSA models with the statistically optimal Wiener filter in the readout that exhibit much higher information capacity, in particular for storing analog data. #R##N#The presented theory also applies to memory buffers, networks with gradual forgetting, which can operate on infinite data streams without memory overflow. Interestingly, we find that different forgetting mechanisms, such as attenuating recurrent weights or neural nonlinearities, produce very similar behavior if the forgetting time constants are aligned. Such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size. These results enable the design of new types of VSA models for the online processing of data streams.

Includes members:
  Member "integer echo state networks hyperdimensional reservoir computing":
    also connected through other hyperedges:
      (none)
  Member "associative long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep quaternion features for privacy protection")
  Member "openxbow introducing the passau open source crossmodal bag of words toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data")
  Member "distributed sequence memory of multidimensional inputs in recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiplex visibility graphs to investigate recurrent neural network dynamics")
  Member "representing objects relations and sequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "density encoding enables resource efficient randomly connected neural networks")
  Member "short term memory capacity in networks via the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed sequence memory of multidimensional inputs in recurrent networks")
  Member "quantum computation via sparse distributed representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "theory of the superposition principle for randomized connectionist representations in neural networks")
  Member "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)