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: "clinical productivity system a decision support model"
  Abstract: Purpose – The aim of this study was to evaluate the effects of a data‐driven clinical productivity system that leverages Electronic Health Record (EHR) data to provide productivity decision support functionality in a real‐world clinical setting. The system was implemented for a large behavioral health care provider seeing over 75,000 distinct clients a year.Design/methodology/approach – The key metric in this system is a “VPU”, which simultaneously optimizes multiple aspects of clinical care. The resulting mathematical value of clinical productivity was hypothesized to tightly link the organization's performance to its expectations and, through transparency and decision support tools at the clinician level, affect significant changes in productivity, quality, and consistency relative to traditional models of clinical productivity.Findings – In only three months, every single variable integrated into the VPU system showed significant improvement, including a 30 percent rise in revenue, 10 percent rise in c...

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence framework for simulating clinical decision making a markov decision process approach"):
  with members:
    - "data mining session based patient reported outcomes pros in a mental health setting toward data driven clinical decision support and personalized treatment"
    - "data mining and electronic health records selecting optimal clinical treatments in practice"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-2 (induced by source paper "ehrs connect research and practice where predictive modeling artificial intelligence and clinical decision support intersect"):
  with members:
    - "data mining session based patient reported outcomes pros in a mental health setting toward data driven clinical decision support and personalized treatment"

Connected through hyperedge HE-3 (induced by source paper "data mining session based patient reported outcomes pros in a mental health setting toward data driven clinical decision support and personalized treatment"):
  with members:
    - "data mining and electronic health records selecting optimal clinical treatments in practice"

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.DB(Databases)