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: "automated reporting of gui design violations for mobile apps"
  Abstract: The inception of a mobile app often takes form of a mock-up of the Graphical User Interface (GUI), represented as a static image delineating the proper layout and style of GUI widgets that satisfy requirements. Following this initial mock-up, the design artifacts are then handed off to developers whose goal is to accurately implement these GUIs and the desired functionality in code. Given the sizable abstraction gap between mock-ups and code, developers often introduce mistakes related to the GUI that can negatively impact an app's success in highly competitive marketplaces. Moreover, such mistakes are common in the evolutionary context of rapidly changing apps. This leads to the time-consuming and laborious task of design teams verifying that each screen of an app was implemented according to intended design specifications.   This paper introduces a novel, automated approach for verifying whether the GUI of a mobile app was implemented according to its intended design. Our approach resolves GUI-related information from both implemented apps and mock-ups and uses computer vision techniques to identify common errors in the implementations of mobile GUIs. We implemented this approach for Android in a tool called G vt  and carried out both a controlled empirical evaluation with open-source apps as well as an industrial evaluation with designers and developers from Huawei. The results show that G vt  solves an important, difficult, and highly practical problem with remarkable efficiency and accuracy and is both useful and scalable from the point of view of industrial designers and developers. The tool is currently used by over one-thousand industrial designers & developers at Huawei to improve the quality of their mobile apps.

Connected through hyperedge HE-1 (induced by source paper "guicomp a gui design assistant with real time multi faceted feedback"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "a computational method for evaluating ui patterns"
    - "pix2code generating code from a graphical user interface screenshot"
    - "learning visual importance for graphic designs and data visualizations"

Connected through hyperedge HE-2 (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"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android 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-3 (induced by source paper "amoga a static dynamic model generation strategy for mobile apps testing"):
  with members:
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-4 (induced by source paper "overcoming language dichotomies toward effective program comprehension for mobile app development"):
  with members:
    - "fusion a tool for facilitating and augmenting android bug reporting"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "on device bug reporting for android applications"
    - "how do developers test android applications"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "mdroid a mutation testing framework for android"
    - "pix2code generating code from a graphical user interface screenshot"
    - "an app performance optimization advisor for mobile device app marketplaces"

Connected through hyperedge HE-5 (induced by source paper "detecting and summarizing gui changes in evolving mobile apps"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "how do developers test android applications"
    - "crashscope a practical tool for automated testing of android applications"
    - "automatically discovering reporting and reproducing android application crashes"
    - "automated test input generation for android are we there yet"

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.SE(Software Engineering)