human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "reducing guesswork via an unreliable oracle":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight bounds on the r enyi entropy via majorization with applications to guessing and compression")
  Member "design and analysis of stability guaranteed pufs":
    also connected through other hyperedges:
      (none)
  Member "multi user guesswork and brute force security":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mismatched guesswork")
  Member "the henchman problem measuring secrecy by the minimum distortion in a list":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strategic communication with side information at the decoder")
  Member "guessing a password over a wireless channel on the effect of noise non uniformity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving guessing random additive noise decoding")
  Member "guessing under source uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized bayesian cramer rao inequality via information geometry of relative alpha entropy")

Question: Please tell me which class the source paper 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)