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: "automatically discovering reporting and reproducing android application crashes"
  Abstract: Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. 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, CRASHSCOPE 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). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.

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"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning driven test generation for android gui applications using formal specifications"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "logically constrained neural fitted q iteration"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "guigle a gui search engine for android apps"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "mining android app usages for generating actionable gui based execution scenarios"

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"
    - "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-5 (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"
    - "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-6 (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"
    - "crashscope a practical tool for automated testing of android applications"
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-7 (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"
    - "crashscope a practical tool for automated testing of android applications"
    - "enabling mutation testing for android apps"
    - "adsplit separating smartphone advertising from applications"
    - "quire lightweight provenance for smart phone operating systems"

Connected through hyperedge HE-8 (induced by source paper "efficiently manifesting asynchronous programming errors in android apps"):
  with members:
    - "large scale analysis of framework specific exceptions in android apps"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "enabling mutation testing for android apps"
    - "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)