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: "guesswork subject to a total entropy budget"
  Abstract: We consider an abstraction of computational security in password protected systems where a user draws a secret string of given length with i.i.d. characters from a finite alphabet, and an adversary would like to identify the secret string by querying, or guessing, the identity of the string. The concept of a "total entropy budget" on the chosen word by the user is natural, otherwise the chosen password would have arbitrary length and complexity. One intuitively expects that a password chosen from the uniform distribution is more secure. This is not the case, however, if we are considering only the average guesswork of the adversary when the user is subject to a total entropy budget. The optimality of the uniform distribution for the user's secret string holds when we have also a budget on the guessing adversary. We suppose that the user is subject to a "total entropy budget" for choosing the secret string, whereas the computational capability of the adversary is determined by his "total guesswork budget." We study the regime where the adversary's chances are exponentially small in guessing the secret string chosen subject to a total entropy budget. We introduce a certain notion of uniformity and show that a more uniform source will provide better protection against the adversary in terms of his chances of success in guessing the secret string. In contrast, the average number of queries that it takes the adversary to identify the secret string is smaller for the more uniform secret string subject to the same total entropy budget.

Connected through hyperedge HE-1 (induced by source paper "mismatched guesswork"):
  with members:
    - "universal randomized guessing with application to asynchronous decentralized brute force attacks"
    - "why botnets work distributed brute force attacks need no synchronization"
    - "a characterization of guesswork on swiftly tilting curves"
    - "centralized vs decentralized multi agent guesswork"
    - "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 "universal randomized guessing with application to asynchronous decentralized brute force attacks"):
  with members:
    - "why botnets work distributed brute force attacks need no synchronization"
    - "centralized vs decentralized multi agent guesswork"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "why botnets work distributed brute force attacks need no synchronization"):
  with members:
    - "a characterization of guesswork on swiftly tilting curves"
    - "centralized vs decentralized multi agent guesswork"
    - "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"
    - "guessing revisited a large deviations approach"

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)