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: "brute force searching the typical set and guesswork"
  Abstract: Consider the situation where a word is chosen probabilistically from a finite list. If an attacker knows the list and can inquire about each word in turn, then selecting the word via the uniform distribution maximizes the attacker's difficulty, its Guesswork, in identifying the chosen word. It is tempting to use this property in cryptanalysis of computationally secure ciphers by assuming coded words are drawn from a source's typical set and so, for all intents and purposes, uniformly distributed within it. By applying recent results on Guesswork, for i.i.d. sources it is this equipartition ansatz that we investigate here. In particular, we demonstrate that the expected Guesswork for a source conditioned to create words in the typical set grows, with word length, at a lower exponential rate than that of the uniform approximation, suggesting use of the approximation is ill-advised.

Connected through hyperedge HE-1 (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"
    - "guesswork large deviations and shannon entropy"
    - "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-2 (induced by source paper "a characterization of guesswork on swiftly tilting curves"):
  with members:
    - "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"
    - "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"
    - "guessing under source uncertainty"

Connected through hyperedge HE-3 (induced by source paper "a simplified coding scheme for the broadcast channel with complementary receiver side information under individual secrecy constraints"):
  with members:
    - "individual secrecy for broadcast channels with receiver side information"
    - "individual secrecy for the broadcast channel"
    - "secret key generation using correlated sources and channels"
    - "secrecy via sources and channels"
    - "the wiretap channel with feedback encryption over the channel"
    - "secret communication with feedback"

Connected through hyperedge HE-4 (induced by source paper "centralized vs decentralized multi agent guesswork"):
  with members:
    - "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"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "guessing under source uncertainty"

Connected through hyperedge HE-5 (induced by source paper "multi user guesswork and brute force security"):
  with members:
    - "encoding tasks and renyi entropy"
    - "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"
    - "lists that are smaller than their parts a coding approach to tunable secrecy"
    - "the shannon cipher system with a guessing wiretapper general sources"
    - "guessing revisited a large deviations approach"
    - "guessing based on length functions"

Connected through hyperedge HE-6 (induced by source paper "quantifying the computational security of multi user systems"):
  with members:
    - "encoding tasks and renyi entropy"
    - "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"
    - "lists that are smaller than their parts a coding approach to tunable secrecy"
    - "the shannon cipher system with a guessing wiretapper general sources"
    - "guessing revisited a large deviations approach"
    - "guessing based on length 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)