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: "password cracking the effect of hash function bias on the average guesswork"
  Abstract: Modern authentication systems store hashed values of passwords of users using cryptographic hash functions. Therefore, to crack a password an attacker needs to guess a hash function input that is mapped to the hashed value, as opposed to the password itself. We call a hash function that maps the same number of inputs to each bin, as \textbf{unbiased}. However, cryptographic hash functions in use have not been proven to be unbiased (i.e., they may have an unequal number of inputs mapped to different bins). A cryptographic hash function has the property that it is computationally difficult to find an input mapped to a bin. In this work we introduce a structured notion of biased hash functions for which we analyze the average guesswork under certain types of brute force attacks. This work shows that the level of security depends on the set of hashed values of valid users as well as the statistical profile of a hash function, resulting from bias. We examine the average guesswork conditioned on the set of hashed values, and model the statistical profile through the empirical distribution of the number of inputs that are mapped to a bin. In particular, we focus on a class of statistical profiles (capturing the bias) , which we call type-class statistical profiles, that has an empirical distribution related to the probability of the type classes defined in the method of types. For such profiles, we show that the average guesswork is related to basic measures in information theory such as entropy and divergence. We use this to show that the effect of bias on the conditional average guesswork is limited compared to other system parameters such as the number of valid users who store their hashed passwords in the system.

Connected through hyperedge HE-1 (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"
    - "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"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"

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.CR(Cryptography and Security)