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: "a monte carlo study of pairwise comparisons"
  Abstract: Consistent approximations obtained by geometric means ($GM$) and the principal eigenvector ($EV$), turned out to be close enough for 1,000,000 not-so-inconsistent pairwise comparisons matrices. In this respect both methods are accurate enough for most practical applications. As the enclosed Table 1 demonstrates, the biggest difference between average deviations of $GM$ and $EV$ solutions is 0.00019 for the Euclidean metric and 0.00355 for the Tchebychev metric. #R##N#For practical applications, this precision is far better than expected. After all we are talking, in most cases, about relative subjective comparisons and one tenth of a percent is usually below our threshold of perception.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of the pairwise comparisons inconsistency reduction process"):
  with members:
    - "on axiomatization of inconsistency indicators for pairwise comparisons"
    - "pairwise comparisons simplified"
    - "on distance based inconsistency reduction algorithms for pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "on axiomatization of inconsistency indicators for pairwise comparisons"):
  with members:
    - "studying a set of properties of inconsistency indices for pairwise comparisons"
    - "axiomatic properties of inconsistency indices for pairwise comparisons"
    - "on distance based inconsistency reduction algorithms for pairwise comparisons"

Connected through hyperedge HE-3 (induced by source paper "pairwise comparisons simplified"):
  with members:
    - "on axiomatization of inconsistency indicators for pairwise comparisons"
    - "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.OH(Other Computer Science)