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 "outstanding bit error tolerance of resistive ram based binarized neural networks"):
  Abstract: Resistive random access memories (RRAM) are novel nonvolatile memory technologies, which can be embedded at the core of CMOS, and which could be ideal for the in-memory implementation of deep neural networks. A particularly exciting vision is using them for implementing Binarized Neural Networks (BNNs), a class of deep neural networks with a highly reduced memory footprint. The challenge of resistive memory, however, is that they are prone to device variation, which can lead to bit errors. In this work we show that BNNs can tolerate these bit errors to an outstanding level, through simulations of networks on the MNIST and CIFAR10 tasks. If a standard BNN is used, up to 10^-4 bit error rate can be tolerated with little impact on recognition performance on both MNIST and CIFAR10. We then show that by adapting the training procedure to the fact that the BNN will be operated on error-prone hardware, this tolerance can be extended to a bit error rate of 4x10^-2. The requirements for RRAM are therefore a lot less stringent for BNNs than more traditional applications. We show, based on experimental measurements on a RRAM HfO2 technology, that this result can allow reduce RRAM programming energy by a factor 30.

Includes members:
  Member "in memory and error immune differential rram implementation of binarized deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic computing for hardware implementation of binarized neural networks")
  Member "towards closing the energy gap between hog and cnn features for embedded vision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fixynn efficient hardware for mobile computer vision via transfer learning")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "xnor net imagenet classification using binary convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)