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: "automated test input generation for android are we there yet"
  Abstract: Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android.

Connected through hyperedge HE-1 (induced by source paper "dl droid deep learning based android malware detection using real devices"):
  with members:
    - "improving dynamic analysis of android apps using hybrid test input generation"
    - "android malware detection using deep learning on api method sequences"
    - "emulator vs real phone android malware detection using machine learning"
    - "n gram opcode analysis for android malware detection"
    - "n opcode analysis for android malware classification and categorization"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"
    - "android malware detection an eigenspace analysis approach"
    - "high accuracy android malware detection using ensemble learning"

Connected through hyperedge HE-2 (induced by source paper "to the attention of mobile software developers guess what test your app"):
  with members:
    - "release practices for mobile apps what do users and developers think"
    - "how do developers test android applications"
    - "scripted gui testing of android apps a study on diffusion evolution and fragility"

Connected through hyperedge HE-3 (induced by source paper "on the energy footprint of mobile testing frameworks"):
  with members:
    - "catalog of energy patterns for mobile applications"
    - "using automatic refactoring to improve energy efficiency of android apps"

Connected through hyperedge HE-4 (induced by source paper "amoga a static dynamic model generation strategy for mobile apps testing"):
  with members:
    - "automated reporting of gui design violations for mobile apps"

Connected through hyperedge HE-5 (induced by source paper "diffusing your mobile apps extending in network function virtualization to mobile function offloading"):
  with members:
    - "mamadroid detecting android malware by building markov chains of behavioral models"

Connected through hyperedge HE-6 (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-7 (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"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "classifying and qualifying gui defects"

Connected through hyperedge HE-8 (induced by source paper "program analysis of commodity iot applications for security and privacy challenges and opportunities"):
  with members:
    - "cross app interference threats in smart homes categorization detection and handling"
    - "soteria automated iot safety and security analysis"
    - "incentivized delivery network of iot software updates based on trustless proof of distribution"
    - "peek a boo i see your smart home activities even encrypted"
    - "sensitive information tracking in commodity iot"
    - "iotdots a digital forensics framework for smart environments"
    - "iotsan fortifying the safety of iot systems"
    - "understanding iot security through the data crystal ball where we are now and where we are going to be"

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)