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 "pimball binary neural networks in spintronic memory"):
  Abstract: Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency or hardware/software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU, GPU, and FPGA based implementations while delivering higher throughput.

Includes members:
  Member "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "neural cache bit serial in cache acceleration of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerating generalized linear models with mlweaving a one size fits all system for any precision learning technical report")
  Member "energy efficient hybrid stochastic binary neural networks for near sensor computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a stochastic computing based deep learning framework using adiabatic quantum flux parametron superconducting technology")
  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 "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  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")
  Member "xnor net imagenet classification using binary convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)