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: "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.

Connected through hyperedge HE-1 (induced by source paper "hyperembed tradeoffs between resources and performance in nlp tasks with hyperdimensional computing enabled embedding of n gram statistics"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "neural random projections for language modelling"
    - "subword semantic hashing for intent classification on small datasets"
    - "handling massive n gram datasets efficiently"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "efficient estimation of word representations in vector space"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-2 (induced by source paper "density encoding enables resource efficient randomly connected neural networks"):
  with members:
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "bitwise neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "representing objects relations and sequences"
    - "provably bounded optimal agents"

Connected through hyperedge HE-3 (induced by source paper "autoscaling bloom filter controlling trade off between true and false positives"):
  with members:
    - "theory of the superposition principle for randomized connectionist representations in neural networks"
    - "neural distributed autoassociative memories a survey"
    - "yes no bloom filter a way of representing sets with fewer false positives"
    - "don t thrash how to cache your hash on flash"
    - "the deletable bloom filter a new member of the bloom family"

Connected through hyperedge HE-4 (induced by source paper "integer echo state networks hyperdimensional reservoir computing"):
  with members:
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "reservoir computing and extreme learning machines using pairs of cellular automata rules"
    - "deep reservoir computing using cellular automata"
    - "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)