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 large deviations and shannon entropy"
  Abstract: How hard is it to guess a password? Massey showed that a simple function of the Shannon entropy of the distribution from which the password is selected is a lower bound on the expected number of guesses, but one which is not tight in general. In a series of subsequent papers under ever less restrictive stochastic assumptions, an asymptotic relationship as password length grows between scaled moments of the guesswork and specific Renyi entropy was identified. Here, we show that, when appropriately scaled, as the password length grows, the logarithm of the guesswork satisfies a large deviation principle (LDP), providing direct estimates of the guesswork distribution when passwords are long. The rate function governing the LDP possesses a specific, restrictive form that encapsulates underlying structure in the nature of guesswork. Returning to Massey's original observation, a corollary to the LDP shows that expectation of the logarithm of the guesswork is the specific Shannon entropy of the password selection process.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "capacity achieving guessing random additive noise decoding"):
  with members:
    - "multi user guesswork and brute force security"
    - "on the origin of polar coding"
    - "the random coding bound is tight for the average linear code or lattice"
    - "brute force searching the typical set and guesswork"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "frames in outdoor 802 11 wlans provide a hybrid binary symmetric packet erasure channel"
    - "guessing revisited a large deviations approach"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "guessing random additive noise decoding with symbol reliability information srgrand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "a characterization of guesswork on swiftly tilting curves"
    - "on the origin of polar coding"
    - "the random coding bound is tight for the average linear code or lattice"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "guessing based on length functions"
    - "iterative algebraic soft decision list decoding of reed solomon codes"

Connected through hyperedge HE-4 (induced by source paper "guessing random additive noise decoding with soft detection symbol reliability information sgrand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "a characterization of guesswork on swiftly tilting curves"
    - "on the origin of polar coding"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "guessing based on length functions"
    - "iterative algebraic soft decision list decoding of reed solomon codes"

Connected through hyperedge HE-5 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs"):
  with members:
    - "robustness of maximal alpha leakage to side information"
    - "data privacy for a rho recoverable function"
    - "an operational approach to information leakage"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "robust privacy utility tradeoffs under differential privacy and hamming distortion"
    - "privacy aware guessing efficiency"
    - "renyi differential privacy"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"

Connected through hyperedge HE-6 (induced by source paper "tight bounds on the r enyi entropy via majorization with applications to guessing and compression"):
  with members:
    - "on the conditional smooth renyi entropy and its applications in guessing and source coding"
    - "maximum entropy interval aggregations"
    - "distributed task encoding"
    - "reducing guesswork via an unreliable oracle"
    - "guessing attacks on distributed storage systems"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "on the smooth renyi entropy and variable length source coding allowing errors"

Connected through hyperedge HE-7 (induced by source paper "improved bounds on lossless source coding and guessing moments via r e nyi measures"):
  with members:
    - "distributed task encoding"
    - "reducing guesswork via an unreliable oracle"
    - "guessing attacks on distributed storage systems"
    - "sharp bounds on arimoto s conditional r e nyi entropies between two distinct orders"
    - "on empirical cumulant generating functions of code lengths for individual sequences"
    - "f divergence inequalities"
    - "on the smooth renyi entropy and variable length source coding allowing errors"
    - "encoding tasks and renyi entropy"

Connected through hyperedge HE-8 (induced by source paper "guessing with a bit of help"):
  with members:
    - "reducing guesswork via an unreliable oracle"
    - "on the optimal boolean function for prediction under quadratic loss"
    - "password cracking the effect of hash function bias on the average guesswork"
    - "on the entropy of a noisy function"
    - "remarks on the most informative function conjecture at fixed mean"
    - "multi user guesswork and brute force security"
    - "an improved upper bound for the most informative boolean function conjecture"
    - "analysis of boolean functions"

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)