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: "bayesian rose trees"
  Abstract: Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy agglomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.

Connected through hyperedge HE-1 (induced by source paper "scalable hierarchical clustering with tree grafting"):
  with members:
    - "improving coreference resolution by learning entity level distributed representations"
    - "learning global features for coreference resolution"
    - "a cost function for similarity based hierarchical clustering"
    - "imagenet large scale visual recognition challenge"
    - "efficient active algorithms for hierarchical clustering"

Connected through hyperedge HE-2 (induced by source paper "learning hierarchical item categories from implicit feedback data for efficient recommendations and browsing"):
  with members:
    - "using taste groups for collaborative filtering"
    - "progressive em for latent tree models and hierarchical topic detection"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "evaluating the complementarity of taxonomic relation extraction methods across different languages"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "learning concept hierarchies from text corpora using formal concept analysis"

Connected through hyperedge HE-4 (induced by source paper "generating object cluster hierarchies for benchmarking"):
  with members:
    - "hierarchical gaussian mixture model with objects attached to terminal and non terminal dendrogram nodes"
    - "hybrid ant colony optimization in solving multi skill resource constrained project scheduling problem"
    - "openml networked science in machine learning"
    - "imagenet large scale visual recognition challenge"
    - "an overview on clustering methods"
    - "methods of hierarchical clustering"

Connected through hyperedge HE-5 (induced by source paper "hierarchy of groups evaluation using different f score variants"):
  with members:
    - "hierarchical gaussian mixture model with objects attached to terminal and non terminal dendrogram nodes"
    - "an overview on clustering methods"

Connected through hyperedge HE-6 (induced by source paper "online visual analytics of text streams"):
  with members:
    - "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream"

Connected through hyperedge HE-7 (induced by source paper "agglomerative bregman clustering"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

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)