human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "an on chip trainable and the clock less spiking neural network with 1r memristive synapses":
    also connected through other hyperedges:
      (none)
  Member "a case for multiple and parallel rrams as synaptic model for training snns":
    also connected through other hyperedges:
      (none)
  Member "a software equivalent snn hardware using rram array for asynchronous real time learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an on chip trainable and the clock less spiking neural network with 1r memristive synapses")
  Member "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on chip learning in a conventional silicon mosfet based analog hardware neural network")
  Member "device and system level design considerations for analog non volatile memory based neuromorphic architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high accuracy inference in neuromorphic circuits using hardware aware training")
  Member "redesigning commercial floating gate memory for analog computing applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive learning rule for hardware based deep neural networks using electronic synapse devices")

Question: Please tell me which class the source paper 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)