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 "a stochastic computing based deep learning framework using adiabatic quantum flux parametron superconducting technology"):
  Abstract: The Adiabatic Quantum-Flux-Parametron (AQFP) superconducting technology has been recently developed, which achieves the highest energy efficiency among superconducting logic families, potentially 104--105 gain compared with state-of-the-art CMOS. In 2016, the successful fabrication and testing of AQFP-based circuits with the scale of 83,000 JJs have demonstrated the scalability and potential of implementing large-scale systems using AQFP. As a result, it will be promising for AQFP in high-performance computing and deep space applications, with Deep Neural Network (DNN) inference acceleration as an important example.   Besides ultra-high energy efficiency, AQFP exhibits two unique characteristics: the deep pipelining nature since each AQFP logic gate is connected with an AC clock signal, which increases the difficulty to avoid RAW hazards; the second is the unique opportunity of true random number generation (RNG) using a single AQFP buffer, far more efficient than RNG in CMOS. We point out that these two characteristics make AQFP especially compatible with the stochastic computing (SC) technique, which uses a time-independent bit sequence for value representation, and is compatible with the deep pipelining nature. Further, the application of SC has been investigated in DNNs in prior work, and the suitability has been illustrated as SC is more compatible with approximate computations.   This work is the first to develop an SC-based DNN acceleration framework using AQFP technology. The deep-pipelining nature of AQFP circuits translates into the difficulty in designing accumulators/counters in AQFP, which makes the prior design in SC-based DNN not suitable. We overcome this limitation taking into account different properties in CONV and FC layers: (i) the inner product calculation in FC layers has more number of inputs than that in CONV layers; (ii) accurate activation function is critical in CONV rather than FC layers. Based on these observations, we propose (i) accurate integration of summation and activation function in CONV layers using bitonic sorting network and feedback loop, and (ii) low-complexity categorization block for FC layers based on chain of majority gates. For complete design we also develop (i) ultra-efficient stochastic number generator in AQFP, (ii) a high-accuracy sub-sampling (pooling) block in AQFP, and (iii) majority synthesis for further performance improvement and automatic buffer/splitter insertion for requirement of AQFP circuits. Experimental results suggest that the proposed SC-based DNN using AQFP can achieve up to 6.8 × 104 times higher energy efficiency compared to CMOS-based implementation while maintaining 96% accuracy on the MNIST dataset.

Includes members:
  Member "on the universal approximation property and equivalence of stochastic computing based neural networks and binary neural networks":
    also connected through other hyperedges:
      (none)
  Member "vibnn hardware acceleration of bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting oxide based resistive ram variability for bayesian neural network hardware design")
  Member "energy efficient hybrid stochastic binary neural networks for near sensor computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pimball binary neural networks in spintronic memory")
  Member "vlsi implementation of deep neural network using integral stochastic computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "progressive stochastic binarization of deep networks")
  Member "finn a framework for fast scalable binarized neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "ese efficient speech recognition engine with sparse lstm on fpga":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")

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.NE(Neural and Evolutionary Computing)