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: "mining android app usages for generating actionable gui based execution scenarios"
  Abstract: GUI-based models extracted from Android app execution traces, events, or source code can be extremely useful for challenging tasks such as the generation of scenarios or test cases. However, extracting effective models can be an expensive process. Moreover, existing approaches for automatically deriving GUI-based models are not able to generate scenarios that include events which were not observed in execution (nor event) traces. In this paper, we address these and other major challenges in our novel hybrid approach, coined as MonkeyLab. Our approach is based on the Record-Mine-Generate-Validate framework, which relies on recording app usages that yield execution (event) traces, mining those event traces and generating execution scenarios using statistical language modeling, static and dynamic analyses, and validating the resulting scenarios using an interactive execution of the app on a real device. The framework aims at mining models capable of generating feasible and fully replayable (i.e., actionable) scenarios reflecting either natural user behavior or uncommon usages (e.g., corner cases) for a given app. We evaluated MONKEYLAB in a case study involving several medium-to-large open-source Android apps. Our results demonstrate that MonkeyLab is able to mine GUI-based models that can be used to generate actionable execution scenarios for both natural and unnatural sequences of events on Google Nexus 7 tablets.

Connected through hyperedge HE-1 (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"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "automatically discovering reporting and reproducing android application crashes"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "helion enabling a natural perspective of home automation"):
  with members:
    - "soteria automated iot safety and security analysis"
    - "sensitive information tracking in commodity iot"
    - "iotsan fortifying the safety of iot systems"
    - "a study of data store based home automation"

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"
    - "automatically discovering reporting and reproducing android application crashes"

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"
    - "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 "machine learning based prototyping of graphical user interfaces for mobile apps"):
  with members:
    - "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"
    - "classifying and qualifying gui defects"

Connected through hyperedge HE-6 (induced by source paper "efficiently manifesting asynchronous programming errors in android apps"):
  with members:
    - "large scale analysis of framework specific exceptions in android apps"
    - "enabling mutation testing for android apps"
    - "automatically discovering reporting and reproducing android application crashes"
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-7 (induced by source paper "how do developers test android applications"):
  with members:
    - "fusion a tool for facilitating and augmenting android bug reporting"
    - "automatically discovering reporting and reproducing android application crashes"
    - "automated test input generation for android are we there yet"
    - "auto completing bug reports for android applications"

Connected through hyperedge HE-8 (induced by source paper "continuous evolutionary and large scale a new perspective for automated mobile app testing"):
  with members:
    - "on device bug reporting for android applications"
    - "enabling mutation testing for android apps"
    - "automatically discovering reporting and reproducing android application crashes"
    - "cloud based augmentation for mobile devices motivation taxonomies and open challenges"

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)