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: "ultra low energy charge trap flash based synapse enabled by parasitic leakage mitigation"
  Abstract: Brain-inspired computation promises complex cognitive tasks at biological energy efficiencies. The brain contains $10^4$ synapses per neuron. Hence, ultra-low energy, high-density synapses are needed for spiking neural networks (SNN). In this paper, we use tunneling enabled CTF (Charge Trap Flash) stack for ultra-low-energy operation (1F); Further, CTF on an SOI platform and back-to-back connected pn diode and Zener diode (2D) prevent parasitic leakage to preserve energy advantage in array operation. A bulk $100 {\mu}m $ x $100 {\mu}m$ CTF operation offers tunable, gradual conductance change $({\Delta}G) i.e. 10^4 $levels, which gives $100$x improvement over literature. SPICE simulations of 1F2D synapse shows ultra-low energy $(\leqslant 3 fJ/pulse)$ at 180 nm node for long-term potentiation (LTP) and depression (LTD), at 180nm node for long-term potentiation (LTP) and depression (LTD), which is comparable to energy estimate in biological synapses (10 fJ). A record low learning rate (i.e., maximum ${\Delta}G 10^6 endurance cycles at full conductance swing) is observed. Such a highly energy efficient synapse with tunable learning rate on the CMOS platform is a key enabler for the human-brain-scale systems. Keywords: Spiking Neural Network; Charge trap flash, SONAS, Fowler-Nordheim Tunneling, Synapse

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)