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 ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
  Abstract: Homeostatic plasticity is a stabilizing mechanism commonly observed in real neural systems that allows neurons to maintain their activity around a functional operating point. This phenomenon can be used in neuromorphic systems to compensate for slowly changing conditions or chronic shifts in the system configuration. However, to avoid interference with other adaptation or learning processes active in the neuromorphic system, it is important that the homeostatic plasticity mechanism operates on time scales that are much longer than conventional synaptic plasticity ones. In this paper we present an ultralow leakage circuit, integrated into an automatic gain control scheme, that can implement the synaptic scaling homeostatic process over extremely long time scales. Synaptic scaling consists in globally scaling the synaptic weights of all synapses impinging onto a neuron maintaining their relative differences, to preserve the effects of learning. The scheme we propose controls the global gain of analog log-domain synapse circuits to keep the neuron's average firing rate constant around a set operating point, over extremely long time scales. To validate the proposed scheme, we implemented the ultralow leakage synaptic scaling homeostatic plasticity circuit in a standard 0.18   $\mu$  m complementary metal-oxide-semiconductor process, and integrated it in an array of dynamic synapses connected to an adaptive integrate and fire neuron. The circuit occupies a silicon area of 84    $\mu$  m   $\times$    22   $\mu$  m and consumes approximately 10.8 nW with a 1.8 V supply voltage. We present experimental results from the homeostatic circuit and demonstrate how it can be configured to exhibit time scales of up to 100 ks, thanks to a controllable leakage current that can be scaled down to 0.45 aA (2.8 electrons per second).

Connected through hyperedge HE-1 (induced by source paper "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"):
  with members:
    - "minimum energy quantized neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "memory and information processing in neuromorphic systems"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"
    - "switched capacitor realization of presynaptic short term plasticity and stop learning synapses in 28 nm cmos"
    - "biologically inspired spiking neurons piecewise linear models and digital implementation"

Connected through hyperedge HE-2 (induced by source paper "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "a differential memristive synapse circuit for on line learning in neuromorphic computing systems"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "deep learning in neural networks"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-3 (induced by source paper "the importance of space and time in neuromorphic cognitive agents"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "adaption toolbox and benchmark for training convolutional neural networks with reduced numerical precision weights and activation"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"

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)