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 "compressive privacy for a linear dynamical system"):
  Abstract: We consider a linear dynamical system in which the state vector consists of both public and private states. One or more sensors make measurements of the state vector and sends information to a fusion center, which performs the final state estimation. To achieve an optimal tradeoff between the utility of estimating the public states and protection of the private states, the measurements at each time step are linearly compressed into a lower dimensional space. Under the centralized setting where all measurements are collected by a single sensor, we propose an optimization problem and an algorithm to find the best compression matrix. Under the decentralized setting where measurements are made separately at multiple sensors, each sensor optimizes its own local compression matrix. We propose methods to separate the overall optimization problem into multiple sub-problems that can be solved locally at each sensor. We consider the cases where there is no message exchange between the sensors; and where each sensor takes turns to transmit messages to the other sensors. Simulations and empirical experiments demonstrate the efficiency of our proposed approach in allowing the fusion center to estimate the public states with good accuracy while preventing it from estimating the private states accurately.

Includes members:
  Member "plausible deniability for privacy preserving data synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "hypothesis testing under maximal leakage privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a practical approach to navigating the tradeoff between privacy and precise utility")
  Member "information theoretic attacks in the smart grid":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning requirements for stealth attacks")
  Member "privacy aware mmse estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "inferential privacy guarantees for differentially private mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentially private model publishing for deep learning")
  Member "differential privacy as a mutual information constraint":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "distributed local linear parameter estimation using gaussian spawn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an unsupervised bayesian neural network for truth discovery")

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)