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: "delay learning architectures for memory and classification"
  Abstract: We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns by changing the delays of every connection as opposed to modifying the weights. The advantage of this architecture over traditional weight based ones is simpler hardware implementation without multipliers or digital-analog converters (DACs) as well as being suited to time-based computing. The name is derived due to similarity in the learning rule with an earlier architecture called Tempotron. The DELTRON can remember more patterns than other delay-based networks by modifying a few delays to remember the most 'salient' or synchronous part of every spike pattern. We present simulations of memory capacity and classification ability of the DELTRON for different random spatio-temporal spike patterns. The memory capacity for noisy spike patterns and missing spikes are also shown. Finally, we present SPICE simulation results of the core circuits involved in a reconfigurable mixed signal implementation of this architecture.

Connected through hyperedge HE-1 (induced by source paper "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"):
  with members:
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "rounding methods for neural networks with low resolution synaptic weights"
    - "deep learning in neural networks"
    - "character aware neural language models"
    - "recurrent models of visual attention"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "racing to learn statistical inference and learning in a single spiking neuron with adaptive kernels"):
  with members:
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"
    - "the ripple pond enabling spiking networks to see"

Connected through hyperedge HE-3 (induced by source paper "hardware amenable structural learning for spike based pattern classification using a simple model of active dendrites"):
  with members:
    - "event driven contrastive divergence for spiking neuromorphic systems"

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)