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: "predicting the popularity of github repositories"
  Abstract: GitHub is the largest source code repository in the world. It provides a git-based source code management platform and also many features inspired by social networks. For example, GitHub users can show appreciation to projects by adding stars to them. Therefore, the number of stars of a repository is a direct measure of its popularity. In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories. These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market. In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months. Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars. Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories. We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).

Connected through hyperedge HE-1 (induced by source paper "is this github project maintained measuring the level of maintenance activity of open source projects"):
  with members:
    - "what s in a github star understanding repository starring practices in a social coding platform"
    - "identifying unmaintained projects in github"
    - "why we engage in floss answers from core developers"
    - "why modern open source projects fail"
    - "understanding the factors that impact the popularity of github repositories"
    - "robust classification for imprecise environments"

Connected through hyperedge HE-2 (induced by source paper "massive multi agent data driven simulations of the github ecosystem"):
  with members:
    - "predicting and explaining behavioral data with structured feature space decomposition"
    - "understanding the group dynamics and success of teams"
    - "coding together at scale github as a collaborative social network"

Connected through hyperedge HE-3 (induced by source paper "what s in a github star understanding repository starring practices in a social coding platform"):
  with members:
    - "how do developers promote open source projects"
    - "why modern open source projects fail"
    - "a novel approach for estimating truck factors"
    - "understanding the factors that impact the popularity of github repositories"
    - "why we refactor confessions of github contributors"
    - "dynamical classes of collective attention in twitter"
    - "robust classification for imprecise environments"

Connected through hyperedge HE-4 (induced by source paper "who will share my image predicting the content diffusion path in online social networks"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "understanding image virality"
    - "densecap fully convolutional localization networks for dense captioning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "can cascades be predicted"

Connected through hyperedge HE-5 (induced by source paper "why modern open source projects fail"):
  with members:
    - "a novel approach for estimating truck factors"
    - "understanding the factors that impact the popularity of github repositories"
    - "why we refactor confessions of github contributors"

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)