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 framework for extracting semantic guarantees from privacy"):
  Abstract: Statistical privacy views privacy definitions as contracts that guide the behavior of algorithms that take in sensitive data and produce sanitized data. For most existing privacy definitions, it is not clear what they actually guarantee. #R##N#In this paper, we propose the first (to the best of our knowledge) framework for extracting semantic guarantees from privacy definitions. That is, instead of answering narrow questions such as "does privacy definition Y protect X?" the goal is to answer the more general question "what does privacy definition Y protect?" #R##N#The privacy guarantees we can extract are Bayesian in nature and deal with changes in an attacker's beliefs. The key to our framework is an object we call the row cone. Every privacy definition has a row cone, which is a convex set that describes all the ways an attacker's prior beliefs can be turned into posterior beliefs after observing an output of an algorithm satisfying that privacy definition. #R##N#The framework can be applied to privacy definitions or even to individual algorithms to identify the types of inferences they defend against. We illustrate the use of our framework with analyses of several definitions and algorithms for which we can derive previously unknown semantics. These include randomized response, FRAPP, and several algorithms that add integer-valued noise to their inputs.

Includes members:
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locally private hypothesis selection")
  Member "what can we learn privately":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "universally utility maximizing privacy mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "composition attacks and auxiliary information in data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local differential privacy for deep learning")
  Member "a framework for high accuracy privacy preserving mining":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams")

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.DB(Databases)