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: "heuristic rating estimation geometric approach"
  Abstract: Heuristic rating estimation is a newly proposed method that supports decisions analysis based on the use of pairwise comparisons. It allows the ranking values of some alternatives (herein referred to as concepts) to be initially known, whilst ranks for other concepts have yet to be estimated. To calculate the missing ranks it is assumed that the priority of every single concept can be determined as the weighted arithmetic mean of the priorities of all the other concepts. It has been shown that the problem has an admissible solution if the inconsistency of the pairwise comparisons is not too high. The proposed approach adopts heuristics according to which a weighted geometric mean is used to determine the missing priorities. In this approach, despite increased complexity, a solution always exists and its existence does not depend on the inconsistency or reciprocity of the input matrix. Thus, the presented approach might be appropriate for a larger number of problems than previous methods. Moreover, it turns out that the geometric approach, as proposed in the article, can be optimal. The optimality condition is presented in the form of a corresponding theorem. A formal definition of the proposed geometric heuristics is accompanied by two numerical examples.

Connected through hyperedge HE-1 (induced by source paper "on the geometric mean method for incomplete pairwise comparisons"):
  with members:
    - "inconsistency in the ordinal pairwise comparisons method with and without ties"
    - "on normalization of inconsistency indicators in pairwise comparisons"
    - "on the properties of the priority deriving procedure in the pairwise comparisons method"

Connected through hyperedge HE-2 (induced by source paper "when condition of order preservation is met"):
  with members:
    - "notes on order preservation and consistency in ahp"
    - "on the properties of the priority deriving procedure in the pairwise comparisons method"
    - "heuristic rating estimation approach to the pairwise comparisons method"
    - "on axiomatization of inconsistency indicators for pairwise comparisons"

Connected through hyperedge HE-3 (induced by source paper "when is the condition of order preservation met"):
  with members:
    - "notes on order preservation and consistency in ahp"
    - "on the properties of the priority deriving procedure in the pairwise comparisons method"
    - "heuristic rating estimation approach to the pairwise comparisons method"
    - "on axiomatization of inconsistency indicators for pairwise comparisons"

Connected through hyperedge HE-4 (induced by source paper "inconsistency in the ordinal pairwise comparisons method with and without ties"):
  with members:
    - "on the properties of the priority deriving procedure in the pairwise comparisons method"
    - "an lp based inconsistency monitoring of pairwise comparison matrices"

Connected through hyperedge HE-5 (induced by source paper "on the properties of the priority deriving procedure in the pairwise comparisons method"):
  with members:
    - "notes on order preservation and consistency in ahp"
    - "heuristic rating estimation approach to the pairwise comparisons method"
    - "on axiomatization of inconsistency indicators for pairwise comparisons"
    - "on distance based inconsistency reduction algorithms for pairwise comparisons"

Connected through hyperedge HE-6 (induced by source paper "notes on order preservation and consistency in ahp"):
  with members:
    - "managing null entries in pairwise comparisons"
    - "heuristic rating estimation approach to the pairwise comparisons method"
    - "on distance based inconsistency reduction algorithms for pairwise comparisons"

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.DM(Discrete Mathematics)