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 "crypto oriented neural architecture design"):
  Abstract: As neural networks revolutionize many applications, significant privacy concerns emerge. Owners of private data wish to use remote neural network services while ensuring their data cannot be interpreted by others. Service providers wish to keep their model private to safeguard its intellectual property. Such privacy conflicts may slow down the adoption of neural networks in sensitive domains such as healthcare. #R##N#Privacy issues have been addressed in the cryptography community in the context of secure computation. However, secure computation protocols have known performance issues. E.g., runtime of secure inference in deep neural networks is three orders of magnitude longer comparing to non-secure inference. Therefore, much research efforts address the optimization of cryptographic protocols for secure inference. #R##N#We take a complementary approach, and provide design principles for optimizing the crypto-oriented neural network architectures to reduce the runtime of secure inference. The principles are evaluated on three state-of-the-art architectures: SqueezeNet, ShuffleNetV2, and MobileNetV2. Our novel method significantly improves the efficiency of secure inference on common evaluation metrics.

Includes members:
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  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 "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "security and privacy issues in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "shufflenet v2 practical guidelines for efficient cnn architecture design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "chameleon a hybrid secure computation framework for machine learning applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cryptanalytic extraction of neural network models")
  Member "tapas tricks to accelerate encrypted prediction as a service":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure evaluation of quantized neural networks")
  Member "gazelle a low latency framework for secure neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep quaternion features for privacy protection")

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.LG(Machine Learning)