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: "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"
  Abstract: Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language present to theories of brain function. The essence of these problems is the question of how to neurally instantiate the rapid construction and transformation of the compositional structures that are typically taken to be the domain of symbolic processing.  He contended that typical connectionist approaches fail to meet these challenges and that the dialogue between linguistic theory and cognitive neuroscience will be relatively unproductive until the importance of these problems is widely recognised and the challenges answered by some technical innovation in connectionist modelling.  This paper claims that a little-known family of connectionist models (Vector Symbolic Architectures) are able to meet Jackendoff's challenges.

Connected through hyperedge HE-1 (induced by source paper "recursive sketches for modular deep learning"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "regularized evolution for image classifier architecture search"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "recovery guarantees for one hidden layer neural networks"
    - "learning to reason end to end module networks for visual question answering"
    - "dynamic routing between capsules"
    - "electron proton dynamics in deep learning"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "resonator circuits for factoring high dimensional vectors"):
  with members:
    - "robust computation with rhythmic spike patterns"
    - "introduction to online convex optimization"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "a theory of sequence indexing and working memory in recurrent neural networks"):
  with members:
    - "integer echo state networks hyperdimensional reservoir computing"
    - "associative long short term memory"
    - "openxbow introducing the passau open source crossmodal bag of words toolkit"
    - "distributed sequence memory of multidimensional inputs in recurrent networks"
    - "representing objects relations and sequences"
    - "short term memory capacity in networks via the restricted isometry property"
    - "quantum computation via sparse distributed representation"

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"
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "theory of the superposition principle for randomized connectionist representations in neural networks"):
  with members:
    - "associative long short term memory"
    - "representing objects relations and sequences"
    - "neural turing machines"
    - "quantum computation via sparse distributed representation"

Connected through hyperedge HE-6 (induced by source paper "representing objects relations and sequences"):
  with members:
    - "natural language processing almost from scratch"

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)