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 software vulnerability detection with machine learning"
  Abstract: Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often manifest themselves in subtle ways that are not obvious to code reviewers or the developers themselves. With the wealth of open source code available for analysis, there is an opportunity to learn the patterns of bugs that can lead to security vulnerabilities directly from data. In this paper, we present a data-driven approach to vulnerability detection using machine learning, specifically applied to C and C++ programs. We first compile a large dataset of hundreds of thousands of open-source functions labeled with the outputs of a static analyzer. We then compare methods applied directly to source code with methods applied to artifacts extracted from the build process, finding that source-based models perform better. We also compare the application of deep neural network models with more traditional models such as random forests and find the best performance comes from combining features learned by deep models with tree-based models. Ultimately, our highest performing model achieves an area under the precision-recall curve of 0.49 and an area under the ROC curve of 0.87.

Connected through hyperedge HE-1 (induced by source paper "vuldeelocator a deep learning based fine grained vulnerability detector"):
  with members:
    - "leopard identifying vulnerable code for vulnerability assessment through program metrics"
    - "sysevr a framework for using deep learning to detect software vulnerabilities"
    - "automated vulnerability detection in source code using deep representation learning"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-3 (induced by source paper "neural bug finding a study of opportunities and challenges"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "neural program repair by jointly learning to localize and repair"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "learning to represent edits"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "generative code modeling with graphs"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-4 (induced by source paper "evaluating semantic representations of source code"):
  with members:
    - "coset a benchmark for evaluating neural program embeddings"
    - "a literature study of embeddings on source code"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "code2seq generating sequences from structured representations of code"
    - "semantic code repair using neuro symbolic transformation networks"

Connected through hyperedge HE-5 (induced by source paper "arducode predictive framework for automation engineering"):
  with members:
    - "a literature study of embeddings on source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "path based function embedding and its application to specification mining"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "beyond textual issues understanding the usage and impact of github reactions"):
  with members:
    - "what s in a github star understanding repository starring practices in a social coding platform"
    - "identifying unmaintained projects in github"
    - "understanding the factors that impact the popularity of github repositories"

Connected through hyperedge HE-7 (induced by source paper "learning to repair software vulnerabilities with generative adversarial networks"):
  with members:
    - "generative adversarial networks an overview"
    - "semantic code repair using neuro symbolic transformation networks"
    - "neural language correction with character based attention"
    - "learning from simulated and unsupervised images through adversarial training"
    - "conditional generative adversarial nets"

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)