human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "mismatched guesswork"):
  with members:
    - "universal randomized guessing with application to asynchronous decentralized brute force attacks"
    - "a characterization of guesswork on swiftly tilting curves"
    - "centralized vs decentralized multi agent guesswork"
    - "guesswork subject to a total entropy budget"
    - "multi user guesswork and brute force security"
    - "list decoding random coding exponents and expurgated exponents"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "erasure list random coding error exponents are not universally achievable"

Connected through hyperedge HE-2 (induced by source paper "noisy guesses"):
  with members:
    - "guessing individual sequences generating randomized guesses using finite state machines"
    - "universal randomized guessing with application to asynchronous decentralized brute force attacks"

Connected through hyperedge HE-3 (induced by source paper "guessing individual sequences generating randomized guesses using finite state machines"):
  with members:
    - "universal randomized guessing with application to asynchronous decentralized brute force attacks"
    - "perfectly secure encryption of individual sequences"

Connected through hyperedge HE-4 (induced by source paper "universal randomized guessing with application to asynchronous decentralized brute force attacks"):
  with members:
    - "centralized vs decentralized multi agent guesswork"
    - "guesswork subject to a total entropy budget"
    - "guessing attacks on distributed storage systems"
    - "multi user guesswork and brute force security"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "investigating the distribution of password choices"

Question: Please tell me which class the center node 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)