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 sample complexity upper and lower bounds for exact ranking from noisy comparisons"):
  Abstract: This paper studies the problem of finding the exact ranking from noisy comparisons. A comparison over a set of $m$ items produces a noisy outcome about the most preferred item, and reveals some information about the ranking. By repeatedly and adaptively choosing items to compare, we want to fully rank the items with a certain confidence, and use as few comparisons as possible. Different from most previous works, in this paper, we have three main novelties: (i) compared to prior works, our upper bounds (algorithms) and lower bounds on the sample complexity (aka number of comparisons) require the minimal assumptions on the instances, and are not restricted to specific models; (ii) we give lower bounds and upper bounds on instances with unequal noise levels; and (iii) this paper aims at the exact ranking without knowledge on the instances, while most of the previous works either focus on approximate rankings or study exact ranking but require prior knowledge. We first derive lower bounds for pairwise ranking (i.e., compare two items each time), and then propose (nearly) optimal pairwise ranking algorithms. We further make extensions to listwise ranking (i.e., comparing multiple items each time). Numerical results also show our improvements against the state of the art.

Includes members:
  Member "from pac to instance optimal sample complexity in the plackett luce model":
    also connected through other hyperedges:
      (none)
  Member "active ranking with subset wise preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "approximate ranking from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "swapped face detection using deep learning and subjective assessment")
  Member "adaptive sampling for coarse ranking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pac ranking from pairwise and listwise queries lower bounds and upper bounds")
  Member "a nearly instance optimal algorithm for top k ranking under the multinomial logit model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "maximum selection and ranking under noisy comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "active ranking from pairwise comparisons and when parametric assumptions don t help":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ranking a set of objects a graph based least square 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.LG(Machine Learning)