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 "shredder learning noise distributions to protect inference privacy"):
  Abstract: Sheer amount of computation in deep neural networks has pushed their execution to the cloud. This de facto cloud-hosted inference, however, raises serious privacy concerns as private data is communicated and stored in remote servers. The data could be mishandled by cloud providers, used for unsolicited analytics, or simply compromised through network and system security vulnerability. To that end, this paper devises SHREDDER that reduces the information content of the communicated data without diminishing the cloud's ability to maintain acceptably high accuracy. To that end, SHREDDER learns two sets of noise distributions whose samples, named multiplicative and additive noise tensors, are applied to the communicated data while maintaining the inference accuracy. The key idea is that SHREDDER learns these noise distributions offline without altering the topology or the weights of the pre-trained network. SHREDDER repeatedly learns sample noise tensors from the distributions by casting the tensors as a set of trainable parameters while keeping the weights constant. Since the key idea is learning the noise, we are able to devise a loss function that strikes a balance between accuracy and information degradation. To this end, we use self-supervision to train the noise tensors to achieve an intermediate representation of the data that contains less private information. Experimentation with real-world deep neural networks shows that, compared to the original execution, SHREDDER reduces the mutual information between the input and the communicated data by 66.90%, and yields a misclassification rate of 94.5% over private labels, significantly reducing adversary's ability to infer private data, while sacrificing only 1.74% loss in accuracy without any knowledge about the private labels.

Includes members:
  Member "using self supervised learning can improve model robustness and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  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 "gazelle a low latency framework for secure neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep quaternion features for privacy protection")
  Member "security risks in deep learning implementations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial machine learning industry perspectives")
  Member "self supervised learning for spinal mris":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally private hypothesis selection")
  Member "how transferable are features in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)