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: "crashscope a practical tool for automated testing of android applications"
  Abstract: Unique challenges arise when testing mobile applications due to their prevailing event-driven nature and complex contextual features (e.g. sensors, notifications). Current automated input generation approaches for Android apps are typically not practical for developers to use due to required instrumentation or platform dependence and generally do not effectively exercise contextual features. To better support developers in mobile testing tasks, in this demo we present a novel, automated tool called C rash S cope . This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, C rash S cope  generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). Results of preliminary studies show that C rash S cope  is able to uncover about as many crashes as other state of the art tools, while providing detailed useful crash reports and test scripts to developers. Website: www.android-dev-tools.com/crashscope-home Video url: https://youtu.be/ii6S1JF6xDw

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"
    - "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"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (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"
    - "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"
    - "classifying and qualifying gui defects"

Connected through hyperedge HE-3 (induced by source paper "detecting and summarizing gui changes in evolving mobile apps"):
  with members:
    - "automated reporting of gui design violations for mobile apps"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "how do developers test android applications"
    - "automatically discovering reporting and reproducing android application crashes"
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-4 (induced by source paper "discovering flaws in security focused static analysis tools for android using systematic mutation"):
  with members:
    - "horndroid practical and sound static analysis of android applications by smt solving"
    - "enabling mutation testing for android apps"
    - "automatically discovering reporting and reproducing android application crashes"
    - "adsplit separating smartphone advertising from applications"
    - "quire lightweight provenance for smart phone operating systems"

Connected through hyperedge HE-5 (induced by source paper "large scale analysis of framework specific exceptions in android apps"):
  with members:
    - "androvault constructing knowledge graph from millions of android apps for automated analysis"
    - "enabling mutation testing for android apps"
    - "droidleaks benchmarking resource leak bugs for android applications"
    - "automatically discovering reporting and reproducing android application crashes"

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"
    - "automatically discovering reporting and reproducing android application crashes"
    - "auto completing bug reports for android applications"

Connected through hyperedge HE-7 (induced by source paper "scripted gui testing of android apps a study on diffusion evolution and fragility"):
  with members:
    - "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)