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 "quantifying consensus of rankings based on q support patterns"):
  Abstract: Rankings, representing preferences over a set of candidates, are widely used in many information systems, e.g., group decision making. It is of great importance to evaluate the consensus of the obtained rankings from multiple agents. There is often no ground truth available for a ranking task. An overall measure of the consensus degree enables us to have a clear cognition about the ranking data. Moreover, it could provide a quantitative indicator for consensus comparison between groups and further improvement of a ranking system. In this paper, a novel consensus quantifying approach, without the need for any correlation or distance functions, is proposed based on a concept of q-support patterns of rankings. The q-support patterns represent the commonality embedded in a set of rankings. A method for detecting outliers in a set of rankings is naturally derived from the proposed consensus quantifying approach. Experimental studies are conducted to demonstrate the effectiveness of the proposed approach.

Includes members:
  Member "maximal rank correlation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of metrics of distance and correlation between ranked lists for compositionality detection")
  Member "optimizing positional scoring rules for rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "maximal rank correlation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study of metrics of distance and correlation between ranked lists for compositionality detection")
  Member "a weighted correlation index for rankings with ties":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "micro and macro level churn analysis of large scale mobile games")
  Member "a family of rank similarity measures based on maximized effectiveness difference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "query driven algorithm selection in early stage retrieval")

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.AI(Artificial Intelligence)