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: "universal randomized guessing with application to asynchronous decentralized brute force attacks"
  Abstract: Consider the problem of guessing the realization of a random vector $\textbf{X}$ by repeatedly submitting queries (guesses) of the form "Is $\textbf{X}$ equal to $\textbf{x}$?" until an affirmative answer is obtained. In this setup, a key figure of merit is the number of queries required until the right vector is identified, a number that is termed the \emph{guesswork}. Typically, one wishes to devise a guessing strategy which minimizes a certain guesswork moment. #R##N#In this work, we study a universal, decentralized scenario where the guesser does not know the distribution of $\textbf{X}$, and is not allowed to use a strategy which prepares a list of words to be guessed in advance, or even remember which words were already used. Such a scenario is useful, for example, if bots within a Botnet carry out a brute-force attack in order to guess a password or decrypt a message, yet cannot coordinate the guesses between them or even know how many bots actually participate in the attack. #R##N#We devise universal decentralized guessing strategies, first, for memoryless sources, and then generalize them for finite-state sources. In each case, we derive the guessing exponent, and then prove its asymptotic optimality by deriving a compatible converse bound. The strategies are based on randomized guessing using a universal distribution. We also extend the results to guessing with side information. Finally, for all above scenarios, we design efficient algorithms in order to sample from the universal distributions, resulting in strategies which do not depend on the source distribution, are efficient to implement, and can be used asynchronously by multiple agents.

Connected through hyperedge HE-1 (induced by source paper "mismatched guesswork"):
  with members:
    - "why botnets work distributed brute force attacks need no synchronization"
    - "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"
    - "why botnets work distributed brute force attacks need no synchronization"

Connected through hyperedge HE-3 (induced by source paper "guessing individual sequences generating randomized guesses using finite state machines"):
  with members:
    - "why botnets work distributed brute force attacks need no synchronization"
    - "perfectly secure encryption of individual sequences"

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)