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 "bicycle attacks considered harmful quantifying the damage of widespread password length leakage"):
  Abstract: We examine the issue of password length leakage via encrypted traffic i.e., bicycle attacks. We aim to quantify both the prevalence of password length leakage bugs as well as the potential harm to users. In an observational study, we find that {\em most} of the Alexa top 100 rates sites are vulnerable to bicycle attacks meaning that an eavesdropping attacker can infer the exact length of a password based on the length the encrypted packet containing the password. We discuss several ways in which an eavesdropping attacker could link this password length with a particular user account e.g., a targeted campaign against a smaller group of users or via DNS hijacking for larger scale campaigns. We next use a decision-theoretic model to quantify the extent to which password length leakage might help an attacker to crack user passwords. In our analysis, we consider three different levels of password attackers: hacker, criminal and nation-state. In all cases, we find that such an attacker who knows the length of each user password gains a significant advantage over one without knowing the password length. As part of this analysis, we also release a new differentially private password frequency dataset from the 2016 LinkedIn breach using a differentially private algorithm of Blocki et al. (NDSS 2016) to protect user accounts. The LinkedIn frequency corpus is based on over 170 million passwords making it the largest frequency corpus publicly available to password researchers. While the defense against bicycle attacks is straightforward (i.e., ensure that passwords are always padded before encryption), we discuss several practical challenges organizations may face when attempting to patch this vulnerability. We advocate for a new W3C standard on how password fields are handled which would effectively eliminate most instances of password length leakage.

Includes members:
  Member "spaced repetition and mnemonics enable recall of multiple strong passwords":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "update tolerant and revocable password backup extended version")
  Member "cash a cost asymmetric secure hash algorithm for optimal password protection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safekeeper protecting web passwords using trusted execution environments")
  Member "can t you hear me knocking identification of user actions on android apps via traffic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale mobile app identification using deep learning")
  Member "naturally rehearsing passwords":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "usability of humanly computable passwords")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "de anonymizing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")

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)