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 "multi user guesswork and brute force security"):
  Abstract: The guesswork problem was originally motivated by a desire to quantify computational security for single user systems. Leveraging recent results from its analysis, we extend the remit and utility of the framework to the quantification of the computational security of multi-user systems. In particular, assume that    $V$    users independently select strings stochastically from a finite, but potentially large, list. An inquisitor who does not know which strings have been selected wishes to identify    $U$    of them. The inquisitor knows the selection probabilities of each user and is equipped with a method that enables the testing of each (user, string) pair, one at a time, for whether that string had been selected by that user. Here, we establish that, unless    $U=V$   , there is no general strategy that minimizes the distribution of the number of guesses, but in the asymptote as the strings become long we prove the following: by construction, there is an asymptotically optimal class of strategies; the number of guesses required in an asymptotically optimal strategy satisfies a large deviation principle with a rate function, which is not necessarily convex, that can be determined from the rate functions of optimally guessing individual users’ strings; if all users’ selection statistics are identical, the exponential growth rate of the average guesswork as the string-length increases is determined by the specific Renyi entropy of the string-source with parameter    $(V-U+1)/(V-U+2)$   , generalizing the known    $V=U=1$    case; and that the Shannon entropy of the source is a lower bound on the average guesswork growth rate for all    $U$    and    $V$   , thus providing a bound on computational security for multi-user systems. Examples are presented to illustrate these results and their ramifications for systems design.

Includes members:
  Member "encoding tasks and renyi entropy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotic expansions of smooth r e nyi entropies and their applications")
  Member "guesswork large deviations and shannon entropy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mismatched guesswork")
  Member "brute force searching the typical set and guesswork":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving guessing random additive noise decoding")
  Member "guessing a password over a wireless channel on the effect of noise non uniformity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving guessing random additive noise decoding")
  Member "investigating the distribution of password choices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "passlab a password security tool for the blue team")
  Member "lists that are smaller than their parts a coding approach to tunable secrecy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "principal inertia components and applications")
  Member "the shannon cipher system with a guessing wiretapper general sources":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tight bounds on the r enyi entropy via majorization with applications to guessing and compression")
  Member "guessing revisited a large deviations approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "practical estimation of renyi entropy")

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)