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: "temperature insensitive analog vector by matrix multiplier based on 55 nm nor flash memory cells"
  Abstract: We have fabricated and successfully tested an analog vector-by-matrix multiplier, based on redesigned 10x12 arrays of 55 nm commercial NOR flash memory cells. The modified arrays enable high-precision individual analog tuning of each cell, with sub-1% accuracy, while keeping the highly optimized cells, with their long-term state retention, intact. The array has an area of 0.33 um^2 per cell, and is at least one order of magnitude more dense than the reported prior implementations of nonvolatile analog memories. The demonstrated vector-by-vector multiplier, using gate coupling to additional periphery cells, has ~2% precision, limited by the aggregate effect of cell noise, retention, mismatch, process variations, tuning precision, and capacitive crosstalk. A differential version of the multiplier has allowed us to demonstrate sub-3% temperature drift of the output signal in the range between 25C and 85C.

Connected through hyperedge HE-1 (induced by source paper "fq conv fully quantized convolution for efficient and accurate inference"):
  with members:
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "deep neural network compression with single and multiple level quantization"
    - "label refinery improving imagenet classification through label progression"
    - "deep residual learning for small footprint keyword spotting"
    - "the high dimensional geometry of binary neural networks"
    - "hello edge keyword spotting on microcontrollers"

Connected through hyperedge HE-2 (induced by source paper "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"):
  with members:
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "in datacenter performance analysis of a tensor processing unit"
    - "sub 1 us sub 20 nj pattern classification in a mixed signal circuit based on embedded 180 nm floating gate memory cell arrays"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "precise deep neural network computation on imprecise low power analog hardware"
    - "jpeg image compression using discrete cosine transform a survey"

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)