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: "patterns of effort contribution and demand and user classification based on participation patterns in npm ecosystem"
  Abstract: Background: Open source requires participation of volunteer and commercial developers (users) in order to deliver functional high-quality components. Developers both contribute effort in the form of patches and demand effort from the component maintainers to resolve issues reported against it. Aim: Identify and characterize patterns of effort contribution and demand throughout the open source supply chain and investigate if and how these patterns vary with developer activity; identify different groups of developers; and predict developers' company affiliation based on their participation patterns. Method: 1,376,946 issues and pull-requests created for 4433 NPM packages with over 10,000 monthly downloads and full (public) commit activity data of the 272,142 issue creators is obtained and analyzed and dependencies on NPM packages are identified. Fuzzy c-means clustering algorithm is used to find the groups among the users based on their effort contribution and demand patterns, and Random Forest is used as the predictive modeling technique to identify their company affiliations. Result: Users contribute and demand effort primarily from packages that they depend on directly with only a tiny fraction of contributions and demand going to transitive dependencies. A significant portion of demand goes into packages outside the users' respective supply chains (constructed based on publicly visible version control data). Three and two different groups of users are observed based on the effort demand and effort contribution patterns respectively. The Random Forest model used for identifying the company affiliation of the users gives a AUC-ROC value of 0.68. Conclusion: Our results give new insights into effort demand and supply at different parts of the supply chain of the NPM ecosystem and its users and suggests the need to increase visibility further upstream.

Connected through hyperedge HE-1 (induced by source paper "detecting and characterizing bots that commit code"):
  with members:
    - "an exploratory study of bot commits"
    - "a dataset and an approach for identity resolution of 38 million author ids extracted from 2b git commits"
    - "which pull requests get accepted and why a study of popular npm packages"
    - "dissecting a social botnet growth content and influence in twitter"

Connected through hyperedge HE-2 (induced by source paper "an exploratory study of bot commits"):
  with members:
    - "detecting and characterizing bots that commit code"
    - "dissecting a social botnet growth content and influence in twitter"

Connected through hyperedge HE-3 (induced by source paper "deriving a usage independent software quality metric"):
  with members:
    - "understanding the factors that impact the popularity of github repositories"
    - "data analysis with bayesian networks a bootstrap approach"

Connected through hyperedge HE-4 (induced by source paper "which pull requests get accepted and why a study of popular npm packages"):
  with members:
    - "understanding the factors that impact the popularity of github repositories"
    - "an insight into the pull requests of github"

Connected through hyperedge HE-5 (induced by source paper "a dataset and an approach for identity resolution of 38 million author ids extracted from 2b git commits"):
  with members:
    - "deriving a usage independent software quality metric"

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)