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: "efficient active algorithms for hierarchical clustering"
  Abstract: Advances in sensing technologies and the growth of the internet have resulted in an explosion in the size of modern datasets, while storage and processing power continue to lag behind. This motivates the need for algorithms that are efficient, both in terms of the number of measurements needed and running time. To combat the challenges associated with large datasets, we propose a general framework for active hierarchical clustering that repeatedly runs an off-the-shelf clustering algorithm on small subsets of the data and comes with guarantees on performance, measurement complexity and runtime complexity. We instantiate this framework with a simple spectral clustering algorithm and provide concrete results on its performance, showing that, under some assumptions, this algorithm recovers all clusters of size ?(log n) using O(n log^2 n) similarities and runs in O(n log^3 n) time for a dataset of n objects. Through extensive experimentation we also demonstrate that this framework is practically alluring.

Connected through hyperedge HE-1 (induced by source paper "guaranteed scalable learning of latent tree models"):
  with members:
    - "beyond physical connections tree models in human pose estimation"
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "tensor decompositions for learning latent variable models"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-2 (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"
    - "bayesian rose trees"

Connected through hyperedge HE-3 (induced by source paper "hierarchical clustering objective functions and algorithms"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering with structural constraints"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "on the practically interesting instances of maxcut"
    - "a uniqueness theorem for clustering"
    - "improved spectral norm bounds for clustering"

Connected through hyperedge HE-4 (induced by source paper "scalable latent tree model and its application to health analytics"):
  with members:
    - "beyond physical connections tree models in human pose estimation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "tensor decompositions for learning latent variable models"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "interactive bayesian hierarchical clustering"):
  with members:
    - "local algorithms for interactive clustering"
    - "active clustering robust and efficient hierarchical clustering using adaptively selected similarities"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-7 (induced by source paper "active distance based clustering using k medoids"):
  with members:
    - "active clustering with model based uncertainty reduction"

Connected through hyperedge HE-8 (induced by source paper "are you going to the party depends who else is coming learning hidden group dynamics via conditional latent tree models"):
  with members:
    - "extracting hidden groups and their structure from streaming interaction data"
    - "learning the latent state space of time varying graphs"
    - "a tensor approach to learning mixed membership community models"
    - "community detection in networks with node attributes"

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)