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: "is spiking logic the route to memristor based computers"
  Abstract: Memristors have been suggested as a novel route to neuromorphic computing based on the similarity between neurons (synapses and ion pumps) and memristors. The D.C. action of the memristor is a current spike, which we think will be fruitful for building memristor computers. In this paper, we introduce 4 different logical assignations to implement sequential logic in the memristor and introduce the physical rules, summation, `bounce-back', directionality and `diminishing returns', elucidated from our investigations. We then demonstrate how memristor sequential logic works by instantiating a NOT gate, an AND gate and a Full Adder with a single memristor. The Full Adder makes use of the memristor's memory to add three binary values together and outputs the value, the carry digit and even the order they were input in.

Connected through hyperedge HE-1 (induced by source paper "spiking memristor logic gates are a type of time variant perceptron"):
  with members:
    - "connecting spiking neurons to a spiking memristor network changes the memristor dynamics"
    - "the short term memory d c response of the memristor demonstrates the causes of the memristor frequency effect"
    - "evolving spiking networks with variable resistive memories"
    - "boolean logic gates from a single memristor via low level sequential logic"

Connected through hyperedge HE-2 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "unsupervised learning in neuromemristive systems"
    - "a nonparametric framework for quantifying generative inference on neuromorphic systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
    - "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"

Connected through hyperedge HE-3 (induced by source paper "quantitative transformation for implementation of adder circuits in physical systems"):
  with members:
    - "the emergence and dynamical evolution of complex transport networks from simple low level behaviours"
    - "physarum chip developments in growing computers from slime mould"
    - "sensory fusion in physarum polycephalum and implementing multi sensory functional computation"
    - "slime mould logic gates based on frequency changes of electrical potential oscillation"
    - "towards slime mould chemical sensor mapping chemical inputs onto electrical potential dynamics of physarum polycephalum"
    - "towards slime mould colour sensor recognition of colours by physarum polycephalum"
    - "slime mould tactile sensor"
    - "boolean logic gate design principles in unconventional computers an nmr case study"

Connected through hyperedge HE-4 (induced by source paper "slime mould memristors"):
  with members:
    - "towards slime mould chemical sensor mapping chemical inputs onto electrical potential dynamics of physarum polycephalum"
    - "towards slime mould colour sensor recognition of colours by physarum polycephalum"
    - "slime mould tactile sensor"
    - "evolution of plastic learning in spiking networks via memristive connections"
    - "slime mould computes planar shapes"

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.ET(Emerging Technologies)