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 "developing non stochastic privacy preserving policies using agglomerative clustering"):
  Abstract: We consider a non-stochastic privacy-preserving problem in which an adversary aims to infer sensitive information $S$ from publicly accessible data $X$ without using statistics. We consider the problem of generating and releasing a quantization $\hat{X}$ of $X$ to minimize the privacy leakage of $S$ to $\hat{X}$ while maintaining a certain level of utility (or, inversely, the quantization loss). The variables $S$ and $S$ are treated as bounded and non-probabilistic, but are otherwise general. We consider two existing non-stochastic privacy measures, namely the maximum uncertainty reduction $L_0(S \rightarrow \hat{X})$ and the refined information $I_*(S; \hat{X})$ (also called the maximin information) of $S$. For each privacy measure, we propose a corresponding agglomerative clustering algorithm that converges to a locally optimal quantization solution $\hat{X}$ by iteratively merging elements in the alphabet of $X$. To instantiate the solution to this problem, we consider two specific utility measures, the worst-case resolution of $X$ by observing $\hat{X}$ and the maximal distortion of the released data $\hat{X}$. We show that the value of the maximin information $I_*(S; \hat{X})$ can be determined by dividing the confusability graph into connected subgraphs. Hence, $I_*(S; \hat{X})$ can be reduced by merging nodes connecting subgraphs. The relation to the probabilistic information-theoretic privacy is also studied by noting that the G{a}cs-K{o}rner common information is the stochastic version of $I_*$ and indicates the attainability of statistical indistinguishability.

Includes members:
  Member "robustness of maximal alpha leakage to side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs")
  Member "a submodularity based agglomerative clustering algorithm for the privacy funnel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the privacy funnel from the viewpoint of local differential privacy")
  Member "tunable measures for information leakage and applications to privacy utility tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "r enyi fair inference")
  Member "privacy preserving prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "uncertain wiretap channels and secure estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variable length coding for zero error channel capacity")
  Member "notes on information theoretic privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimation efficiency under privacy constraints")
  Member "from the information bottleneck to the privacy funnel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "a nonstochastic information theory for communication and state estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a signal space distance measure for nondispersive optical fiber")

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)