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 "secure evaluation of quantized neural networks"):
  Abstract: Image classification using Deep Neural Networks that preserve the privacy of both the input image and the model being used, has received considerable attention in the last couple of years. Recent work in this area have shown that it is possible to perform image classification with realistically sized networks using e.g., Garbled Circuits as in XONN (USENIX '19) or MPC (CrypTFlow, Eprint '19). These, and other prior work, require models to be either trained in a specific way or postprocessed in order to be evaluated securely. We contribute to this line of research by showing that this postprocessing can be handled by standard Machine Learning frameworks. More precisely, we show that quantization as present in Tensorflow suffices to obtain models that can be evaluated directly and as-is in standard off-the-shelve MPC. We implement secure inference of these quantized models in MP-SPDZ, and the generality of our technique means we can demonstrate benchmarks for a wide variety of threat models, something that has not been done before. In particular, we provide a comprehensive comparison between running secure inference of large ImageNet models with active and passive security, as well as honest and dishonest majority. The most efficient inference can be performed using a passive honest majority protocol which takes between 0.9 and 25.8 seconds, depending on the size of the model; for active security and an honest majority, inference is possible between 9.5 and 147.8 seconds.

Includes members:
  Member "cryptflow secure tensorflow inference":
    also connected through other hyperedges:
      (none)
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "xonn xnor based oblivious deep neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "quantizing deep convolutional networks for efficient inference a whitepaper":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "training and inference with integers in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "a survey on methods and theories of quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "training deep neural networks with 8 bit floating point numbers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "chameleon a hybrid secure computation framework for machine learning applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cryptanalytic extraction of neural network models")

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.CR(Cryptography and Security)