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: "identifying relevant information cues for vulnerability assessment using cvss"
  Abstract: The assessment of new vulnerabilities is an activity that accounts for information from several data sources and produces a 'severity' score for the vulnerability. The Common Vulnerability Scoring System (CVSS) is the reference standard for this assessment. Yet, no guidance currently exists on which information aids a correct assessment and should therefore be considered. In this paper we address this problem by evaluating which information cues increase (or decrease) assessment accuracy. We devise a block design experiment with 67 software engineering students with varying vulnerability information and measure scoring accuracy under different information sets. We find that baseline vulnerability descriptions provided by standard vulnerability sources provide only part of the information needed to achieve an accurate vulnerability assessment. Further, we find that additional information on assets, attacks, and vulnerability type contributes in increasing the accuracy of the assessment; conversely, information on known threats misleads the assessor and decreases assessment accuracy and should be avoided when assessing vulnerabilities. These results go in the direction of formalizing the vulnerability communication to, for example, fully automate security assessments.

Connected through hyperedge HE-1 (induced by source paper "an empirical assessment of security risks of global android banking apps"):
  with members:
    - "a large scale empirical study on industrial fake apps"
    - "storydroid automated generation of storyboard for android apps"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
    - "efficient estimation of word representations in vector space"

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)