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 "why botnets work distributed brute force attacks need no synchronization"):
  Abstract: In September 2017, McAffee Labs quarterly report estimated that brute force attacks represent 20% of total network attacks, making them the most prevalent type of attack ex-aequo with browser based vulnerabilities. These attacks have sometimes catastrophic consequences, and understanding their fundamental limits may play an important role in the risk assessment of password-secured systems, and in the design of better security protocols. While some solutions exist to prevent online brute-force attacks that arise from one single IP address, attacks performed by botnets are more challenging. In this paper, we analyze these distributed attacks by using a simplified model. Our aim is to understand the impact of distribution and asynchronization on the overall computational effort necessary to breach a system. Our result is based on Guesswork, a measure of the number of password queries (guesses) before the correct one is found in an optimal attack, which is a direct surrogate for the time and the computational effort. We model the lack of synchronization by a worst-case optimization in which the queries are received in the worst possible order, resulting in a min-max formulation. We show that even without synchronization and for sequences of growing length, the asymptotic optimal performance is achievable by using randomized guesses drawn from an appropriate distribution. Therefore, randomization is key for distributed asynchronous attacks. In other words, asynchronous guessers can asymptotically perform brute-force attacks as efficiently as synchronized guessers.

Includes members:
  Member "a characterization of guesswork on swiftly tilting curves":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair resource allocation in federated learning")
  Member "centralized vs decentralized multi agent guesswork":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mismatched guesswork")
  Member "guesswork subject to a total entropy budget":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mismatched guesswork")
  Member "guessing attacks on distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tight bounds on the r enyi entropy via majorization with applications to guessing and compression")
  Member "multi user guesswork and brute force security":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "finite sample analysis of image registration")
  Member "guesswork large deviations and shannon entropy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mismatched guesswork")
  Member "guessing a password over a wireless channel on the effect of noise non uniformity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving guessing random additive noise decoding")

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)