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: "ausera large scale automated security risk assessment of global mobile banking apps"
  Abstract: Contemporary financial technology (FinTech) that enables cashless mobile payment has been widely adopted by financial institutions, such as banks, due to its convenience and efficiency. However, FinTech has also made massive and dynamic transactions susceptible to security risks. Given large financial losses caused by such vulnerabilities, regulatory technology (RegTech) has been developed, but more comprehensive security risk assessment is specifically desired to develop robust, scalable, and efficient financial activities. #R##N#In this paper, we undertake the first automated security risk assessment and focus on global banking apps to examine FinTech. First, we analyze a large number of banking apps and propose a comprehensive set of security weaknesses widely existent in these apps. Second, we design a three-phase automated security risk assessment system (Ausera), which combines natural language processing and static analysis of data and control flows, to efficiently identify security weaknesses of banking apps. We performed experiments on 693 real-world banking apps across over 80 countries and unveiled 2,157 weaknesses. To date, 21 banks have acknowledged the weaknesses that we reported. We find that outdated version of banking apps, pollution from third-party libraries, and weak hash functions are all likely to be exploited by attackers. We also show that banking apps of different provenance exhibit various types of security weaknesses, mainly due to economies and regulations that take shape. Given the drastic change in the nature of intermediation, it behooves the RegTech companies and all stakeholders to understand the characteristics and consequences of security risks brought by contemporary FinTech.

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"
    - "identifying relevant information cues for vulnerability assessment using cvss"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "core automating review recommendation for code changes"):
  with members:
    - "an empirical assessment of security risks of global android banking apps"
    - "pythia ai assisted code completion system"
    - "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"
    - "storydroid automated generation of storyboard for android apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-3 (induced by source paper "storydroid automated generation of storyboard for android apps"):
  with members:
    - "a large scale empirical study on industrial fake apps"
    - "understanding android obfuscation techniques a large scale investigation in the wild"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "automated reporting of gui design violations for mobile apps"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "pix2code generating code from a graphical user interface screenshot"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"

Connected through hyperedge HE-4 (induced by source paper "a large scale empirical study on industrial fake apps"):
  with members:
    - "storydroid automated generation of storyboard for android 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"

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)