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: "a manually curated dataset of fixes to vulnerabilities of open source software"
  Abstract: Advancing our understanding of software vulnerabilities, automating their identification, the analysis of their impact, and ultimately their mitigation is necessary to enable the development of software that is more secure. While operating a vulnerability assessment tool that we developed and that is currently used by hundreds of development units at SAP, we manually collected and curated a dataset of vulnerabilities of open-source software and the commits fixing them. The data was obtained both from the National Vulnerability Database (NVD) and from project-specific Web resources that we monitor on a continuous basis. From that data, we extracted a dataset that maps 624 publicly disclosed vulnerabilities affecting 205 distinct open-source Java projects, used in SAP products or internal tools, onto the 1282 commits that fix them. Out of 624 vulnerabilities, 29 do not have a CVE identifier at all and 46, which do have a CVE identifier assigned by a numbering authority, are not available in the NVD yet. The dataset is released under an open-source license, together with supporting scripts that allow researchers to automatically retrieve the actual content of the commits from the corresponding repositories and to augment the attributes available for each instance. Also, these scripts allow to complement the dataset with additional instances that are not security fixes (which is useful, for example, in machine learning applications). Our dataset has been successfully used to train classifiers that could automatically identify security-relevant commits in code repositories. The release of this dataset and the supporting code as open-source will allow future research to be based on data of industrial relevance; also, it represents a concrete step towards making the maintenance of this dataset a shared effort involving open-source communities, academia, and the industry.

Connected through hyperedge HE-1 (induced by source paper "learning to catch security patches"):
  with members:
    - "patchnet a tool for deep patch classification"
    - "the coming era of alphahacking a survey of automatic software vulnerability detection exploitation and patching techniques"
    - "a practical approach to the automatic classification of security relevant commits"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "a survey of machine learning for big code and naturalness"
    - "survey of automated vulnerability detection and exploit generation techniques in cyber reasoning systems"
    - "impact of tool support in patch construction"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "exploiting token and path based representations of code for identifying security relevant commits"):
  with members:
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "dropblock a regularization method for convolutional networks"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "beyond metadata code centric and usage based analysis of known vulnerabilities in open source software"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-3 (induced by source paper "commit2vec learning distributed representations of code changes"):
  with members:
    - "a literature study of embeddings on source code"
    - "code2vec learning distributed representations of code"
    - "a practical approach to the automatic classification of security relevant commits"
    - "learning to represent edits"
    - "code2seq generating sequences from structured representations of code"
    - "a survey of machine learning for big code and naturalness"
    - "a convolutional attention network for extreme summarization of source code"
    - "convolutional neural networks over tree structures for programming language processing"

Connected through hyperedge HE-4 (induced by source paper "characterizing and understanding software developer networks in security development"):
  with members:
    - "why software projects need heroes lessons learned from 1100 projects"
    - "we don t need another hero the impact of heroes on software development"
    - "developer prioritization in bug repositories"
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "evolutionary trends of developer coordination a network approach"
    - "categorizing bugs with social networks a case study on four open source software communities"
    - "the rise and fall of a central contributor dynamics of social organization and performance in the gentoo community"

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)