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 "privacy with estimation guarantees"):
  Abstract: We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data. In this setting, we present an estimation-theoretic analysis of the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy). We demonstrate how $\chi^2$-information captures the fundamental PUT in this case and provide bounds for the best PUT. We propose a convex program to compute privacy-assuring mappings when the functions to be disclosed and hidden are known a priori and the data distribution is known. We derive lower bounds on the minimum mean-squared error of estimating a target function from the disclosed data and evaluate the robustness of our approach when an empirical distribution is used to compute the privacy-assuring mappings instead of the true data distribution. We illustrate the proposed approach through two numerical experiments.

Includes members:
  Member "the utility cost of robust privacy guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "generalizing bottleneck problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "obfuscation via information density estimation")
  Member "matching anonymized and obfuscated time series to users profiles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "privacy preserving adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints")
  Member "renyi differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "principal inertia components and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "task based quantization with application to mimo receivers")
  Member "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "an estimation theoretic view of privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")

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)