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 "a fully private pipeline for deep learning on electronic health records"):
  Abstract: We introduce an end-to-end private deep learning framework, applied to the task of predicting 30-day readmission from electronic health records. By using differential privacy during training and homomorphic encryption during inference, we demonstrate that our proposed pipeline could maintain high performance while providing robust privacy guarantees against information leak from data transmission or attacks against the model. We also explore several techniques to address the privacy-utility trade-off in deploying neural networks with privacy mechanisms, improving the accuracy of differentially-private training and the computation cost of encrypted operations using ideas from both machine learning and cryptography.

Includes members:
  Member "the secret sharer measuring unintended neural network memorization extracting secrets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system")
  Member "deep learning for metagenomic data using 2d embeddings and convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "a review of privacy and consent management in healthcare a focus on emerging data sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing iot data and its quality for use")
  Member "identifying diabetic patients with high risk of readmission":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "embpred30 assessing 30 days readmission for diabetic patients using categorical embeddings")
  Member "globally optimal training of generalized polynomial neural networks with nonlinear spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on connected sublevel sets in deep learning")
  Member "faster arithmetic for number theoretic transforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "heaan demystified accelerating fully homomorphic encryption through architecture centric analysis and optimization")

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)