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 "hiding symbols and functions new metrics and constructions for information theoretic security"):
  Abstract: We present information-theoretic definitions and results for analyzing symmetric-key encryption#R##N#schemes beyond the perfect secrecy regime, i.e. when perfect secrecy is not attained.#R##N#We adopt two lines of analysis, one based on lossless source coding, and another akin to ratedistortion#R##N#theory. We start by presenting a new information-theoretic metric for security, called#R##N#ǫ-symbol secrecy, and derive associated fundamental bounds. This metric provides a parameterization#R##N#of secrecy that spans other information-theoretic metrics for security, such as weak#R##N#secrecy and perfect secrecy. We then introduce list-source codes (LSCs), which are a general#R##N#framework for mapping a key length (entropy) to a list size that an eavesdropper has to resolve#R##N#in order to recover a secret message. We provide explicit constructions of LSCs, and show that#R##N#LSCs that achieve high symbol secrecy also achieve a favorable tradeoff between key length and#R##N#uncertainty list size. We also demonstrate that, when the source is uniformly distributed, the#R##N#highest level of symbol secrecy for a fixed key length can be achieved through a construction#R##N#based on minimum-distance separable (MDS) codes. Using an analysis related to rate-distortion#R##N#theory, we then show how symbol secrecy can be used to determine the probability that an eavesdropper#R##N#correctly reconstructs functions of the original plaintext. More specifically, we present#R##N#lower bounds for the minimum-mean-squared-error of estimating a target function of the plaintext#R##N#given that a certain set of functions of the plaintext is known to be hard (or easy) to infer,#R##N#either by design of the security system or by restrictions imposed on the adversary. We illustrate#R##N#how these bounds can be applied to characterize security properties of symmetric-key encryption#R##N#schemes, and, in particular, extend security claims based on symbol secrecy to a functional#R##N#setting. Finally, we discuss the application of our methods in key distribution, storage and#R##N#privacy.

Includes members:
  Member "an exploration of the role of principal inertia components in information theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the courtade kumar conjecture for certain classes of boolean functions")
  Member "utility privacy tradeoffs in databases an information theoretic approach":
    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 "rate distortion theory for secrecy systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy aware distributed hypothesis testing")
  Member "lists that are smaller than their parts a coding approach to tunable secrecy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "principal inertia components and applications")
  Member "universal secure network coding via rank metric codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "roos bound for skew cyclic codes in hamming and rank metric")
  Member "source coding with side information using list decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lists that are smaller than their parts a coding approach to tunable secrecy")
  Member "random linear network coding a free cipher":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "packet loss recovery in broadcast for real time applications in dense wireless networks")

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)