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: "developer prioritization in bug repositories"
  Abstract: Developers build all the software artifacts in development. Existing work has studied the social behavior in software repositories. In one of the most important software repositories, a bug repository, developers create and update bug reports to support software development and maintenance. However, no prior work has considered the priorities of developers in bug repositories. In this paper, we address the problem of the developer prioritization, which aims to rank the contributions of developers. We mainly explore two aspects, namely modeling the developer prioritization in a bug repository and assisting predictive tasks with our model. First, we model how to assign the priorities of developers based on a social network technique. Three problems are investigated, including the developer rankings in products, the evolution over time, and the tolerance of noisy comments. Second, we consider leveraging the developer prioritization to improve three predicted tasks in bug repositories, i.e., bug triage, severity identification, and reopened bug prediction. We empirically investigate the performance of our model and its applications in bug repositories of Eclipse and Mozilla. The results indicate that the developer prioritization can provide the knowledge of developer priorities to assist software tasks, especially the task of bug triage.

Connected through hyperedge HE-1 (induced by source paper "a systematic mapping study of developer social network research"):
  with members:
    - "on using network science in mining developers collaboration in software engineering a systematic literature review"
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "lessons learned from applying social network analysis on an industrial free libre open source software ecosystem"
    - "analysis of the social community based on the network growing model in open source software community"
    - "evolutionary trends of developer coordination a network approach"
    - "converging work talk patterns in online task oriented communities"
    - "categorizing bugs with social networks a case study on four open source software communities"
    - "a quantitative study of social organisation in open source software communities"

Connected through hyperedge HE-2 (induced by source paper "characterizing and understanding software developer networks in security development"):
  with members:
    - "a manually curated dataset of fixes to vulnerabilities of open source software"
    - "why software projects need heroes lessons learned from 1100 projects"
    - "we don t need another hero the impact of heroes on software development"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "train one get one free partially supervised neural network for bug report duplicate detection and clustering"):
  with members:
    - "deeptriage exploring the effectiveness of deep learning for bug triaging"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "bilateral multi perspective matching for natural language sentences"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "a large annotated corpus for learning natural language inference"
    - "lstm based deep learning models for non factoid answer selection"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "a multi label dual output deep neural network for automated bug triaging"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deeptriage exploring the effectiveness of deep learning for bug triaging"
    - "automatic bug triage using semi supervised text classification"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "deeptriage exploring the effectiveness of deep learning for bug triaging"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "distributed representations of sentences and documents"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "a simple nlp based approach to support onboarding and retention in open source communities"):
  with members:
    - "deeptriage exploring the effectiveness of deep learning for bug triaging"

Connected through hyperedge HE-7 (induced by source paper "debt prone bugs technical debt in software maintenance"):
  with members:
    - "automatic bug triage using semi supervised text classification"
    - "solving the large scale next release problem with a backbone based multilevel algorithm"

Connected through hyperedge HE-8 (induced by source paper "on using network science in mining developers collaboration in software engineering a systematic literature review"):
  with members:
    - "link classification and tie strength ranking in online social networks with exogenous interaction networks"

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)