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 "local differential privacy for deep learning"):
  Abstract: The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data, such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. Existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners’ devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: (1) convolutional module, (2) randomization module, and (3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91%-96%) with high model quality even under low privacy budgets (e.g. e=0.5).

Includes members:
  Member "efficient privacy preservation of big data for accurate data mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams")
  Member "an energy driven network function virtualization for multi domain software defined networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an active network based open framework for iot")
  Member "an efficient and scalable privacy preserving algorithm for big data and data streams":
    also connected through other hyperedges:
      (none)
  Member "mvg mechanism differential privacy under matrix valued query":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism")
  Member "efficient data perturbation for privacy preserving and accurate data stream mining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams")
  Member "towards practical differential privacy for sql queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale")
  Member "a hybrid deep learning architecture for privacy preserving mobile analytics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep quaternion features for privacy protection")
  Member "machine learning models that remember too much":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fawkes protecting personal privacy against unauthorized deep 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.LG(Machine Learning)