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 "learning a static bug finder from data"):
  Abstract: Static analysis is an effective technique to catch bugs early when they are easy to fix. Recent advances in program reasoning theory have led to increasing adoption of static analyzers in software engineering practice. Despite the significant progress, there is still potential for improvement. In this paper, we present an alternative approach to create static bug finders. Instead of relying on human expertise, we leverage deep neural networks-which have achieved groundbreaking results in a number of problem domains-to train a static analyzer directly from data. In particular, we frame the problem of bug finding as a classification task and train a classifier to differentiate the buggy from non-buggy programs using Gated Graph Neural Network (GGNN). In addition, we propose a novel interval-based propagation mechanism that significantly improves the generalization of GGNN on larger graphs. We have realized our approach into a framework, NeurSA, and extensively evaluated it. In a cross-project prediction task, three neural bug detectors we instantiate from NeurSA are highly precise in catching null pointer dereference, array index out of bound and class cast bugs in unseen code. A close comparison with Facebook Infer in catching null pointer dereference bugs reveals NeurSA to be far more precise in catching the real bugs and suppressing the spurious warnings. We are in active discussion with Visa Inc for possible adoption of NeurSA in their software development cycle. Due to the effectiveness and generality, we expect NeurSA to be helpful in improving the quality of their code base.

Includes members:
  Member "learning scalable and precise representation of program semantics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning blended precise semantic program embeddings")
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "code vectors understanding programs through embedded abstracted symbolic traces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cc2vec distributed representations of code changes")
  Member "graph partition neural networks for semi supervised classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "learning to represent programs with graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial robustness for code")
  Member "dynamic neural program embedding for program repair":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cc2vec distributed representations of code changes")

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)