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: "enabling mutation testing for android apps"
  Abstract: Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. This paper proposes MDroid+, a framework for effective mutation testing of Android apps. First, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 so ware artifacts from different sources (e.g., bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented an infrastructure to automatically seed mutations in Android apps with 35 of the identified operators. The taxonomy and the proposed operators have been evaluated in terms of stillborn/trivial mutants generated as compared to well know mutation tools, and their capacity to represent real faults in Android apps

Connected through hyperedge HE-1 (induced by source paper "deepmutation a neural mutation tool"):
  with members:
    - "learning how to mutate source code from bug fixes"
    - "massive exploration of neural machine translation architectures"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning how to mutate source code from bug fixes"):
  with members:
    - "assessing test case prioritization on real faults and mutants"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "learning to represent programs with graphs"
    - "learning continuous semantic representations of symbolic expressions"
    - "tailored mutants fit bugs better"
    - "deep api learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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"
    - "crashscope a practical tool for automated testing of android applications"
    - "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 "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"
    - "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 "large scale analysis of framework specific exceptions in android apps"):
  with members:
    - "androvault constructing knowledge graph from millions of android apps for automated analysis"
    - "crashscope a practical tool for automated testing of android applications"
    - "droidleaks benchmarking resource leak bugs for android applications"
    - "automatically discovering reporting and reproducing android application crashes"

Connected through hyperedge HE-7 (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"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "automatically discovering reporting and reproducing android application crashes"
    - "cloud based augmentation for mobile devices motivation taxonomies and open challenges"

Connected through hyperedge HE-8 (induced by source paper "mdroid a mutation testing framework for android"):
  with members:
    - "how do developers test android applications"
    - "a large scale empirical comparison of static and dynamic test case prioritization techniques"
    - "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)