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: "homogeneity of cluster ensembles"
  Abstract: The expectation and the mean of partitions generated by a cluster ensemble are not unique in general. This issue poses challenges in statistical inference and cluster stability. In this contribution, we state sufficient conditions for uniqueness of expectation and mean. The proposed conditions show that a unique mean is neither exceptional nor generic. To cope with this issue, we introduce homogeneity as a measure of how likely is a unique mean for a sample of partitions. We show that homogeneity is related to cluster stability. This result points to a possible conflict between cluster stability and diversity in consensus clustering. To assess homogeneity in a practical setting, we propose an efficient way to compute a lower bound of homogeneity. Empirical results using the k-means algorithm suggest that uniqueness of the mean partition is not exceptional for real-world data. Moreover, for samples of high homogeneity, uniqueness can be enforced by increasing the number of data points or by removing outlier partitions. In a broader context, this contribution can be placed as a further step towards a statistical theory of partitions.

Connected through hyperedge HE-1 (induced by source paper "the mean partition theorem of consensus clustering"):
  with members:
    - "asymptotic behavior of mean partitions in consensus clustering"
    - "properties of the sample mean in graph spaces and the majorize minimize mean algorithm"
    - "geometry of graph edit distance spaces"

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.LG(Machine Learning)