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 "hardware architecture for large parallel array of random feature extractors applied to image recognition"):
  Abstract: We demonstrate a low-power and compact hardware implementation of Random Feature Extractor (RFE) core. With complex tasks like Image Recognition requiring a large set of features, we show how weight reuse technique can allow to virtually expand the random features available from RFE core. Further, we show how to avoid computation cost wasted for propagating "incognizant" or redundant random features. For proof of concept, we validated our approach by using our RFE core as the first stage of Extreme Learning Machine (ELM)--a two layer neural network--and were able to achieve $>97\%$ accuracy on MNIST database of handwritten digits. ELM's first stage of RFE is done on an analog ASIC occupying $5$mm$\times5$mm area in $0.35\mu$m CMOS and consuming $5.95$ $\mu$J/classify while using $\approx 5000$ effective hidden neurons. The ELM second stage consisting of just adders can be implemented as digital circuit with estimated power consumption of $20.9$ nJ/classify. With a total energy consumption of only $5.97$ $\mu$J/classify, this low-power mixed signal ASIC can act as a co-processor in portable electronic gadgets with cameras.

Includes members:
  Member "vlsi extreme learning machine a design space exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a stacked autoencoder neural network based automated feature extraction method for anomaly detection in on line condition monitoring")
  Member "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "track everything limiting prior knowledge in online multi object recognition")
  Member "learning spike time codes through morphological learning with binary synapses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a system of different layers of abstraction for artificial intelligence")
  Member "a neuromorphic hardware framework based on population coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances in physical reservoir computing a review")
  Member "hardware amenable structural learning for spike based pattern classification using a simple model of active dendrites":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an online structural plasticity rule for generating better reservoirs")
  Member "network in network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)