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: "storydroid automated generation of storyboard for android apps"
  Abstract: Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code.

Connected through hyperedge HE-1 (induced by source paper "unblind your apps predicting natural language labels for mobile gui components by deep learning"):
  with members:
    - "attention is all you need"
    - "convolutional image captioning"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
    - "deep residual learning for image recognition"
    - "microsoft coco captions data collection and evaluation server"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-2 (induced by source paper "an empirical assessment of security risks of global android banking apps"):
  with members:
    - "a large scale empirical study on industrial fake apps"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "identifying relevant information cues for vulnerability assessment using cvss"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "core automating review recommendation for code changes"):
  with members:
    - "an empirical assessment of security risks of global android banking apps"
    - "pythia ai assisted code completion system"
    - "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-4 (induced by source paper "a large scale empirical study on industrial fake apps"):
  with members:
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"

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)