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 "averaging attacks on bounded noise based disclosure control algorithms"):
  Abstract: We describe and evaluate an attack that reconstructs the histogram of any target attribute of a sensitive dataset which can only be queried through a specific class of real-world privacy-preserving algorithms which we call bounded perturbation algorithms. A defining property of such an algorithm is that it perturbs answers to the queries by adding zero-mean noise distributed within a bounded (possibly undisclosed) range. Other key properties of the algorithm include only allowing restricted queries (enforced via an online interface), suppressing answers to queries which are only satisfied by a small group of individuals (e.g., by returning a zero as an answer), and adding the same perturbation to two queries which are satisfied by the same set of individuals (to thwart differencing or averaging attacks). A real-world example of such an algorithm is the one deployed by the Australian Bureau of Statistics' (ABS) online tool called TableBuilder, which allows users to create tables, graphs and maps of Australian census data [30]. We assume an attacker (say, a curious analyst) who is given oracle access to the algorithm via an interface. We describe two attacks on the algorithm. Both attacks are based on carefully constructing (different) queries that evaluate to the same answer. The first attack finds the hidden perturbation parameter $r$ (if it is assumed not to be public knowledge). The second attack removes the noise to obtain the original answer of some (counting) query of choice. We also show how to use this attack to find the number of individuals in the dataset with a target attribute value $a$ of any attribute $A$, and then for all attribute values $a_i \in A$. Our attacks are a practical illustration of the (informal) fundamental law of information recovery which states that ``overly accurate estimates of too many statistics completely destroys privacy'' [9, 15].

Includes members:
  Member "diffix birch extending diffix aspen":
    also connected through other hyperedges:
      (none)
  Member "linear program reconstruction in practice":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when the signal is in the noise exploiting diffix s sticky noise")
  Member "when the signal is in the noise exploiting diffix s sticky noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "techniques d anonymisation tabulaire concepts et mise en oeuvre")
  Member "privacy assessment of de identified opal data a report for transport for nsw":
    also connected through other hyperedges:
      (none)
  Member "differentially private release of public transport data the opal use case":
    also connected through other hyperedges:
      (none)
  Member "concentrated differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privately learning markov random fields")
  Member "between pure and approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privately learning markov random fields")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")

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)