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 "strong hardness of privacy from weak traitor tracing"):
  Abstract: Despite much study, the computational complexity of differential privacy remains poorly understood. In this paper we consider the computational complexity of accurately answering a family $Q$ of statistical queries over a data universe $X$ under differential privacy. A statistical query on a dataset $D \in X^n$ asks "what fraction of the elements of $D$ satisfy a given predicate $p$ on $X$?" Dwork et al. (STOC'09) and Boneh and Zhandry (CRYPTO'14) showed that if both $Q$ and $X$ are of polynomial size, then there is an efficient differentially private algorithm that accurately answers all the queries, and if both $Q$ and $X$ are exponential size, then under a plausible assumption, no efficient algorithm exists. #R##N#We show that, under the same assumption, if either the number of queries or the data universe is of exponential size, and the other has size at least $\tilde{O}(n^7)$, then there is no differentially private algorithm that answers all the queries. In both cases, the result is nearly quantitatively tight, since there is an efficient differentially private algorithm that answers $\tilde{\Omega}(n^2)$ queries on an exponential size data universe, and one that answers exponentially many queries on a data universe of size $\tilde{\Omega}(n^2)$. #R##N#Our proofs build on the connection between hardness results in differential privacy and traitor-tracing schemes (Dwork et al., STOC'09; Ullman, STOC'13). We prove our hardness result for a polynomial size query set (resp., data universe) by showing that they follow from the existence of a special type of traitor-tracing scheme with very short ciphertexts (resp., secret keys), but very weak security guarantees, and then constructing such a scheme.

Includes members:
  Member "order revealing encryption and the hardness of private learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private center points and learning of halfspaces")
  Member "differentially private release and learning of threshold functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privately learning markov random fields")
  Member "between pure and approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privately learning markov random fields")
  Member "preventing false discovery in interactive data analysis is hard":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "private learning and sanitization pure vs approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an equivalence between private classification and online prediction")
  Member "private multiplicative weights beyond linear queries":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private stochastic convex optimization with optimal rates")
  Member "interactive fingerprinting codes and the hardness of preventing false discovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "faster private release of marginals on small databases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")

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)