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: "learning a static analyzer from data"
  Abstract: To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. #R##N#In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. #R##N#We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers.

Connected through hyperedge HE-1 (induced by source paper "adversarial robustness for code"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"

Connected through hyperedge HE-2 (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"
    - "storydroid automated generation of storyboard for android apps"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "spark static program analysis reasoning and retrieving knowledge"):
  with members:
    - "convolutional neural networks over tree structures for programming language processing"

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.PL(Programming Languages)