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 "a characterization of guesswork on swiftly tilting curves"):
  Abstract: Given a collection of strings, each with an associated probability of occurrence, the guesswork of each of them is their position in an ordered list from most likely to least likely, breaking ties arbitrarily. Guesswork is central to several applications in information theory: Average guesswork provides a lower bound on the expected computational cost of a sequential decoder to decode successfully the intended message; the complementary cumulative distribution function of guesswork gives the error probability in list decoding; the logarithm of guesswork is the number of bits needed in optimal lossless one-to-one source coding; and guesswork is the number of trials required of an adversary to breach a password protected system in a brute-force attack. In this paper, we consider memoryless string-sources that generate strings consisting of i.i.d. characters drawn from a finite alphabet, and characterize their corresponding guesswork. Our main tool is the tilt operation. We show that the tilt operation on a memoryless string-source parametrizes an exponential family of memoryless string-sources, which we refer to as the tilted family. We provide an operational meaning to the tilted families by proving that two memoryless string-sources result in the same guesswork on all strings of all lengths if and only if their respective categorical distributions belong to the same tilted family. Establishing some general properties of the tilt operation, we generalize the notions of weakly typical set and asymptotic equipartition property to tilted weakly typical sets of different orders. We use this new definition to characterize the large deviations for all atypical strings and characterize the volume of weakly typical sets of different orders. We subsequently build on this characterization to prove large deviation bounds on guesswork and provide an accurate approximation of its PMF.

Includes members:
  Member "multi user guesswork and brute force security":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mismatched guesswork")
  Member "list decoding random coding exponents and expurgated exponents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mismatched guesswork")
  Member "asymptotics and non asymptotics for universal fixed to variable source coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finite sample analysis of image registration")
  Member "guesswork large deviations and shannon entropy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mismatched guesswork")
  Member "brute force searching the typical set and guesswork":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "capacity achieving guessing random additive noise decoding")
  Member "guessing revisited a large deviations approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "practical estimation of renyi entropy")
  Member "guessing under source uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalized bayesian cramer rao inequality via information geometry of relative alpha 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)