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: "slack channels ecology in enterprises how employees collaborate through group chat"
  Abstract: Despite the long history of studying instant messaging usage in organizations, we know very little about how today's people participate in group chat channels and interact with others. In this short note, we aim to update the existing knowledge on how group chat is used in the context of today's organizations. We have the privilege of collecting a total of 4300 publicly available group chat channels in Slack from an R\&D department in a multinational IT company. Through qualitative coding of 100 channels, we identified 9 channel categories such as project based channels and event channels. We further defined a feature metric with 21 features to depict the group communication style for these group chat channels, with which we successfully trained a machine learning model that can automatically classify a given group channel into one of the 9 categories. In addition, we illustrated how these communication metrics could be used for analyzing teams' collaboration activities. We focused on 117 project teams as we have their performance data, and further collected 54 out of the 117 teams' Slack group data and generated the communication style metrics for each of them. With these data, we are able to build a regression model to reveal the relationship between these group communication styles and one indicator of the project team performance.

Connected through hyperedge HE-1 (induced by source paper "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"):
  with members:
    - "automated machine learning via admm"
    - "a formal method for automl via admm"
    - "openml networked science in machine learning"

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.HC(Human-Computer Interaction)