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 "an empirical assessment of security risks of global android banking apps"):
  Abstract: Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps.  Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named AUSERA, which leverages static program analysis techniques and sensitive keyword identification. By leveraging AUSERA, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. To date, we highlight that 21 banks have confirmed the weaknesses we reported. We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.

Includes members:
  Member "a large scale empirical study on industrial fake apps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "characterizing cryptocurrency exchange scams")
  Member "storydroid automated generation of storyboard for android apps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unblind your apps predicting natural language labels for mobile gui components by deep learning")
  Member "ausera large scale automated security risk assessment of global mobile banking apps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "core automating review recommendation for code changes")
  Member "identifying relevant information cues for vulnerability assessment using cvss":
    also connected through other hyperedges:
      (none)
  Member "efficiently manifesting asynchronous programming errors in android apps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "core automating review recommendation for code changes")
  Member "large scale analysis of framework specific exceptions in android apps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "storydroid automated generation of storyboard for android apps")
  Member "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unblind your apps predicting natural language labels for mobile gui components by deep learning")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

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)