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: "auto completing bug reports for android applications"
  Abstract: The modern software development landscape has seen a shift in focus toward mobile applications as tablets and smartphones near ubiquitous adoption. Due to this trend, the complexity of these “apps” has been increasing, making development and maintenance challenging. Additionally, current bug tracking systems are not able to effectively support construction of reports with actionable information that directly lead to a bug’s resolution. To address the need for an improved reporting system, we introduce a novel solution, called FUSION, that helps users auto-complete reproduction steps in bug reports for mobile apps. FUSION links user-provided information to program artifacts extracted through static and dynamic analysis performed before testing or release. The approach that FUSION employs is generalizable to other current mobile software platforms, and constitutes a new method by which off-device bug reporting can be conducted for mobile software projects. In a study involving 28 participants we applied FUSION to support the maintenance tasks of reporting and reproducing defects from 15 real-world bugs found in 14 open source Android apps while qualitatively and qualitatively measuring the user experience of the system. Our results demonstrate that FUSION both effectively facilitates reporting and allows for more reliable reproduction of bugs from reports compared to traditional issue tracking systems by presenting more detailed contextual app information.

Connected through hyperedge HE-1 (induced by source paper "assessing the quality of the steps to reproduce in bug reports"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "ncrf an open source neural sequence labeling toolkit"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "crashscope a practical tool for automated testing of android applications"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "automatically discovering reporting and reproducing android application crashes"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "extracting and analyzing context information in user support conversations on twitter"):
  with members:
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "machine learning based prototyping of graphical user interfaces for mobile apps"):
  with members:
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "pix2code generating code from a graphical user interface screenshot"
    - "crashscope a practical tool for automated testing of android applications"
    - "easy over hard a case study on deep learning"
    - "automatically discovering reporting and reproducing android application crashes"
    - "deep residual learning for image recognition"
    - "automated test input generation for android are we there yet"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "how do developers test android applications"):
  with members:
    - "fusion a tool for facilitating and augmenting android bug reporting"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "automatically discovering reporting and reproducing android application crashes"
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-6 (induced by source paper "on device bug reporting for android applications"):
  with members:
    - "fusion a tool for facilitating and augmenting android bug reporting"
    - "crashscope a practical tool for automated testing of android applications"
    - "automatically discovering reporting and reproducing android application crashes"

Connected through hyperedge HE-7 (induced by source paper "enhancing bug reports for mobile apps"):
  with members:
    - "mining android app usages for generating actionable gui based execution scenarios"

Connected through hyperedge HE-8 (induced by source paper "fusion a tool for facilitating and augmenting android bug reporting"):
  with members:
    - "mining android app usages for generating actionable gui based execution scenarios"

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)