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 "scalable k medoids via true error bound and familywise bandits"):
  Abstract: K-Medoids(KM) is a standard clustering method, used extensively on semi-metric data. Error analyses of KM have traditionally used an in-sample notion of error, which can be far from the true error and suffer from generalization error. We formalize the true K-Medoid error based on the underlying data distribution, by decomposing it into fundamental statistical problems of: minimum estimation (ME) and minimum mean estimation (MME). We provide a convergence result for MME and bound the true KM error for iid data. Inspired by this bound, we propose a computationally efficient, distributed KM algorithm namely MCPAM. MCPAM has expected runtime $\mathcal{O}(km)$ and provides massive computational savings for a small tradeoff in accuracy. We verify the quality and scaling properties of MCPAM on various datasets. And achieve the hitherto unachieved feat of calculating the KM of 1 billion points on semi-metric spaces.

Includes members:
  Member "on learning mixtures of well separated gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "parallel k medoids spatial clustering algorithm based on mapreduce":
    also connected through other hyperedges:
      (none)
  Member "analysis of thompson sampling for the multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "fast approximation of centrality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic approximate shortest paths and beyond subquadratic and worst case update time")
  Member "objective criteria for the evaluation of clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")

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)