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: "machine learning based prototyping of graphical user interfaces for mobile apps"
  Abstract: It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code. This process takes place both at an application's inception and in an evolutionary context as GUI changes keep pace with evolving features. Unfortunately, this practice is challenging and time-consuming. In this paper, we present an approach that automates this process by enabling accurate prototyping of GUIs via three tasks: detection, classification, and assembly. First, logical components of a GUI are detected from a mock-up artifact using either computer vision techniques or mock-up metadata. Then, software repository mining, automated dynamic analysis, and deep convolutional neural networks are utilized to accurately classify GUI-components into domain-specific types (e.g., toggle-button). Finally, a data-driven, K-nearest-neighbors algorithm generates a suitable hierarchical GUI structure from which a prototype application can be automatically assembled. We implemented this approach for Android in a system called ReDraw. Our evaluation illustrates that ReDraw achieves an average GUI-component classification accuracy of 91% and assembles prototype applications that closely mirror target mock-ups in terms of visual affinity while exhibiting reasonable code structure. Interviews with industrial practitioners illustrate ReDraw's potential to improve real development workflows.

Connected through hyperedge HE-1 (induced by source paper "guicomp a gui design assistant with real time multi faceted feedback"):
  with members:
    - "automated reporting of gui design violations 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 "sketch2code generating a website from a paper mockup"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pix2code generating code from a graphical user interface screenshot"
    - "richer convolutional features for edge detection"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "on learning meaningful code changes via neural machine translation"):
  with members:
    - "semantically enhanced software traceability using deep learning techniques"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "learn fuzz machine learning for input fuzzing"
    - "deepsoft a vision for a deep model of software"
    - "a deep learning model for estimating story points"
    - "vocabulary manipulation for neural machine translation"
    - "deep api learning"

Connected through hyperedge HE-4 (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"
    - "automated reporting of gui design violations 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-5 (induced by source paper "guigle a gui search engine for android apps"):
  with members:
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "automatically discovering reporting and reproducing android application crashes"

Connected through hyperedge HE-6 (induced by source paper "an empirical study on learning bug fixing patches in the wild via neural machine translation"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "the adverse effects of code duplication in machine learning models of code"
    - "automatic software repair a bibliography"
    - "deep reinforcement learning for programming language correction"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "dissection of a bug dataset anatomy of 395 patches from defects4j"

Connected through hyperedge HE-7 (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"
    - "automated reporting of gui design violations 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-8 (induced by source paper "detecting and summarizing gui changes in evolving mobile apps"):
  with members:
    - "automated reporting of gui design violations 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)