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 "yerbabuena securing deep learning inference data via enclave based ternary model partitioning"):
  Abstract: Deploying and serving deep learning (DL) models in the public cloud facilitates the process to bootstrap artificial intelligence (AI) services. Yet, preserving the confidentiality of sensitive input data remains a concern to most service users. Accidental disclosures of user input data may breach increasingly stringent data protection regulations and inflict reputation damage. In this paper, we systematically investigate the life cycles of input data in deep learning image classification pipelines and further identify the potential places for information disclosures. Based on the discovered insights, we build YerbaBuena, an enclave-based model serving system to protect the confidentiality and integrity of user input data. To accommodate the performance and capacity limitations of today's enclave technology, we employ a Ternary Model Partitioning strategy that allows service users to securely partition their proprietary DL models on local machines. Therefore, we can (I) enclose sensitive computation in a secure enclave to mitigate input information disclosures and (II) delegate non-sensitive workloads to run out of enclave with hardware-assisted DL acceleration. Our comprehensive partitioning analysis and workload measurement demonstrate how users can automatically determine the optimal partitioning for their models, thus to maximize confidentiality guarantees with low performance costs.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "chiron privacy preserving machine learning as a service":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing privacy preserving outsourced convolutional neural network predictions")
  Member "mlcapsule guarded offline deployment of machine learning as a service":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "survey of attacks and defenses on edge deployed neural networks")
  Member "efficient deep learning on multi source private data":
    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 "the circle game scalable private membership test using trusted hardware":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sgxpectre stealing intel secrets from sgx enclaves via speculative execution")
  Member "opening the black box of deep neural networks via information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "hardidx practical and secure index with sgx":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "encdbdb searchable encrypted fast compressed in memory database using enclaves")
  Member "stealing machine learning models via prediction apis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic backdoor attacks against machine learning 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)