human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on obtaining stable rankings"):
  Abstract: Decision making is challenging when there is more than one criterion to consider. In such cases, it is common to assign a goodness score to each item as a weighted sum of its attribute values and r...

Includes members:
  Member "a nutritional label for rankings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "qr2 a third party query reranking service over web databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rrr rank regret representative")
  Member "rrr rank regret representative":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "responsible scoring mechanisms through function sampling")
  Member "ranking with fairness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair correlation clustering")
  Member "query reranking as a service":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "responsible scoring mechanisms through function sampling")
  Member "discovering the skyline of web databases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rrr rank regret representative")
  Member "crowdsourcing pareto optimal object finding by pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rrr rank regret representative")
  Member "optimal aggregation algorithms for middleware":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding interesting frames in deep video analytics a top k approach")

Question: Please tell me which class the source paper 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)