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: "analysis and design of a passive switched capacitor matrix multiplier for approximate computing"
  Abstract: A switched-capacitor matrix multiplier is presented for approximate computing and machine learning applications. The multiply-and-accumulate operations perform discrete-time charge-domain signal processing using passive switches and 300 aF unit capacitors. The computation is digitized with a 6 b asynchronous successive approximation register analog-to-digital converter. The analyses of incomplete charge accumulation and thermal noise are discussed. The design was fabricated in 40 nm CMOS, and experimental measurements of multiplication are illustrated using matched filtering and image convolutions to analyze noise and offset. Two applications are highlighted: 1) energy-efficient feature extraction layer performing both compression and classification in a neural network for an analog front end and 2) analog acceleration for solving optimization problems that are traditionally performed in the digital domain. The chip obtains measured efficiencies of 8.7 TOPS/W at 1 GHz for the first application and 7.7 TOPS/W at 2.5 GHz for the second application.

Connected through hyperedge HE-1 (induced by source paper "mixed signal charge domain acceleration of deep neural networks through interleaved bit partitioned arithmetic"):
  with members:
    - "development of a sensory neural network for medical diagnosing"
    - "yolov3 an incremental improvement"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "low latency neural speech translation"
    - "recogym a reinforcement learning environment for the problem of product recommendation in online advertising"
    - "safedrive enhancing lane appearance for autonomous and assisted driving under limited visibility"
    - "caterpillar coarse grain reconfigurable architecture for accelerating the training of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "design automation for binarized neural networks a quantum leap opportunity"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "origami a 803 gop s w convolutional network accelerator"
    - "cbinfer change based inference for convolutional neural networks on video data"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "reducing the energy cost of inference via in sensor information processing"
    - "deep learning with energy efficient binary gradient cameras"

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)