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: "redesigning commercial floating gate memory for analog computing applications"
  Abstract: We have modified a commercial NOR flash memory array to enable high-precision tuning of individual floating-gate cells for analog computing applications. The modified array area per cell in a 180 nm process is about 1.5 um^2. While this area is approximately twice the original cell size, it is still at least an order of magnitude smaller than in the state-of-the-art analog circuit implementations. The new memory cell arrays have been successfully tested, in particular confirming that each cell may be automatically tuned, with ~1% precision, to any desired subthreshold readout current value within an almost three-orders-of-magnitude dynamic range, even using an unoptimized tuning algorithm. Preliminary results for a four-quadrant vector-by-matrix multiplier, implemented with the modified memory array gate-coupled with additional peripheral floating-gate transistors, show highly linear transfer characteristics over a broad range of input currents.

Connected through hyperedge HE-1 (induced by source paper "adaptive learning rule for hardware based deep neural networks using electronic synapse devices"):
  with members:
    - "lstm a search space odyssey"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "precise deep neural network computation on imprecise low power analog hardware"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "deep residual learning for image recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "ultra low energy charge trap flash based synapse enabled by parasitic leakage mitigation"):
  with members:
    - "an on chip trainable and the clock less spiking neural network with 1r memristive synapses"
    - "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"

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)