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: "a behavioral compact model for static characteristics of 3d nand flash memory"
  Abstract: We present a behavioral compact model for static characteristics of 3D NAND flash memory for integrated circuits and system-level applications utilizing BSIM-CMG 110.0.0. This model is easy to implement, computationally efficient, fast, accurate, and effectively accounts for the different parasitic capacitance coupling effects applicable to the 3D geometry of the vertical channel Macaroni body charge-trap flash memory. The model parameter extraction methodology is simple and can be extended to reproduce the electrical behavior of different 3D NAND flash memory architectures (with different page size, dimension, or a number of stacked layers). We believe that the developed compact model would equip the circuit designers and system architects with an effective tool for design-exploration of 3D NAND flash memory devices for diverse unconventional analog applications.

Connected through hyperedge HE-1 (induced by source paper "3d acortex an ultra compact energy efficient neurocomputing platform based on commercial 3d nand flash memories"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "discovering low precision networks close to full precision networks for efficient embedded inference"
    - "rapidnn in memory deep neural network acceleration framework"
    - "deep learning for iot big data and streaming analytics a survey"
    - "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)