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 "ranking a set of objects a graph based least square approach"):
  Abstract: We consider the problem of ranking $N$ objects starting from a set of noisy pairwise comparisons provided by a crowd of equal workers. We assume that objects are endowed with intrinsic qualities and that the probability with which an object is preferred to another depends only on the difference between the qualities of the two competitors. We propose a class of non-adaptive ranking algorithms that rely on a least-squares optimization criterion for the estimation of qualities. Such algorithms are shown to be asymptotically optimal (i.e., they require $O(\frac{N}{\epsilon^2}\log \frac{N}{\delta})$ comparisons to be $(\epsilon, \delta)$-PAC). Numerical results show that our schemes are very efficient also in many non-asymptotic scenarios exhibiting a performance similar to the maximum-likelihood algorithm. Moreover, we show how they can be extended to adaptive schemes and test them on real-world datasets.

Includes members:
  Member "graph resistance and learning from pairwise comparisons":
    also connected through other hyperedges:
      (none)
  Member "maximum selection and ranking under noisy comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins")
  Member "active ranking from pairwise comparisons and when parametric assumptions don t help":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on sample complexity upper and lower bounds for exact ranking from noisy comparisons")
  Member "top k ranking from pairwise comparisons when spectral ranking is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning mixtures of plackett luce models from structured partial orders")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial bandits with relative feedback")
  Member "estimation from pairwise comparisons sharp minimax bounds with topology dependence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph resistance and learning from pairwise comparisons")
  Member "least squares ranking on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hodge decomposition and the shapley value of a cooperative game")

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.IR(Information Retrieval)