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 "obfuscation via information density estimation"):
  Abstract: Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined threshold are deemed information-leaking features. Once these features are identified, we sequentially pass them through a targeted obfuscation mechanism with a provable leakage guarantee in terms of $\mathsf{E}_\gamma$-divergence. The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the trimmed information density estimator (TIDE). We then use TIDE to implement our mechanism on three real-world datasets. Our approach can be used as a data-driven pipeline for designing obfuscation mechanisms targeting specific features.

Includes members:
  Member "privacy amplification by mixing and diffusion mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences")
  Member "estimation efficiency under privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "distributed generation of privacy preserving data with user customization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints")
  Member "on the robustness of information theoretic privacy measures and mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multinomial concentration in relative entropy at the ratio of alphabet and sample sizes")
  Member "towards privacy preserving visual recognition via adversarial training a pilot study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "generative adversarial privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "an operational approach to information leakage":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the capacity of single server weakly private information retrieval")
  Member "a tunable measure for information leakage":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a class of parameterized loss functions for classification optimization tradeoffs and robustness characteristics")

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.IT(Information Theory)