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 "an algorithmic framework for differentially private data analysis on trusted processors"):
  Abstract: Differential privacy has emerged as the main definition for private data analysis and machine learning. The global model of differential privacy, which assumes that users trust the data collector, provides strong privacy guarantees and introduces small errors in the output. In contrast, applications of differential privacy in commercial systems by Apple, Google, and Microsoft, use the local model. Here, users do not trust the data collector, and hence randomize their data before sending it to the data collector. Unfortunately, local model is too strong for several important applications and hence is limited in its applicability. In this work, we propose a framework based on trusted processors and a new definition of differential privacy called Oblivious Differential Privacy, which combines the best of both local and global models. The algorithms we design in this framework show interesting interplay of ideas from the streaming algorithms, oblivious algorithms, and differential privacy.

Includes members:
  Member "the privacy blanket of the shuffle model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "amplification by shuffling from local to central differential privacy via anonymity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "distributed differential privacy via shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "practical locally private heavy hitters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "cachezoom how sgx amplifies the power of cache attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "copycat controlled instruction level attacks on enclaves for maximal key extraction")
  Member "software grand exposure sgx cache attacks are practical":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed systems and trusted execution environments trade offs and challenges")
  Member "towards practical differential privacy for sql queries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale")
  Member "prochlo strong privacy for analytics in the crowd":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pure differentially private summation from anonymous messages")

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)