human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural bug finding a study of opportunities and challenges"):
  Abstract: Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analyses is difficult and requires high expertise, which results in only a few experts able to write such analyses. This paper explores the opportunities and challenges of an alternative way of creating static bug detectors: neural bug finding. The basic idea is to formulate bug detection as a classification problem, and to address this problem with neural networks trained on examples of buggy and non-buggy code. We systematically study the effectiveness of this approach based on code examples labeled by a state-of-the-art, static bug detector. Our results show that neural bug finding is surprisingly effective for some bug patterns, sometimes reaching a precision and recall of over 80%, but also that it struggles to understand some program properties obvious to a traditional analysis. A qualitative analysis of the results provides insights into why neural bug finders sometimes work and sometimes do not work. We also identify pitfalls in selecting the code examples used to train and validate neural bug finders, and propose an algorithm for selecting effective training data.

Includes members:
  Member "modeling vocabulary for big code machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "neural program repair by jointly learning to localize and repair":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to fix build errors with graph2diff neural networks")
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial robustness for code")
  Member "a general path based representation for predicting program properties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structural language models of code")
  Member "learning to represent edits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structural language models of code")
  Member "vuldeepecker a deep learning based system for vulnerability detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "generative code modeling with graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial robustness for code")
  Member "path based function embedding and its application to specification mining":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning execution through neural code fusion")

Question: Please tell me which class the source paper 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)