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: "an on chip trainable and the clock less spiking neural network with 1r memristive synapses"
  Abstract: Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable  r esistive RAM or RRAM array serving as synaptic weights and neuronal drivers in the periphery is an attractive option for the SNN. Recognition (akin to “reading” the synaptic weight) requires  small  amplitude bias applied across the RRAM to minimize conductance change. Learning (akin to “writing” or updating the synaptic weight) requires  large  amplitude bias pulses to produce a conductance change. The contradictory bias amplitude requirement to perform reading and writing  simultaneously  and  asynchronously , akin to biology, is a major challenge. Solutions suggested in the literature rely on time-division-multiplexing of read and write operations based on clocks, or approximations ignoring the reading when coincidental with writing. In this paper, we overcome this challenge and present a clock-less approach wherein reading and writing are performed in different frequency domains. This enables learning and recognition simultaneously on an SNN. We validate our scheme in SPICE circuit simulator by translating a two-layered feed-forward Iris classifying SNN to demonstrate software-equivalent performance. The system performance is not adversely affected by a voltage dependence of conductance in realistic RRAMs, despite departing from linearity. Overall, our approach enables direct implementation of biological SNN algorithms in hardware.

Connected through hyperedge HE-1 (induced by source paper "ultra low energy charge trap flash based synapse enabled by parasitic leakage mitigation"):
  with members:
    - "a case for multiple and parallel rrams as synaptic model for training snns"
    - "a software equivalent snn hardware using rram array for asynchronous real time learning"
    - "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"
    - "device and system level design considerations for analog non volatile memory based neuromorphic architectures"
    - "redesigning commercial floating gate memory for analog computing applications"

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)