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: "fast detection of overlapping communities via online tensor methods"
  Abstract: We present a fast tensor-based approach for detecting hidden overlapping communities under the Mixed Membership Stochastic Blockmodel (MMSB). We present two implementations, \viz a GPU-based implementation which exploits the parallelism of SIMD architectures and a CPU-based implementation for larger datasets, wherein the GPU memory does not suffice. Our GPU-based implementation involves a careful optimization of storage, data transfer and matrix computations. Our CPU-based implementation involves sparse linear algebraic operations which exploit the data sparsity. We use stochastic gradient descent for multilinear spectral optimization and this allows for flexibility in the tradeoff between node sub-sampling and accuracy of the results. We validate our results on datasets from Facebook, Yelp and DBLP where ground truth is available, using notions of $p$-values and false discovery rates, and obtain high accuracy for membership recovery. We compare our results, both in terms of execution time and accuracy, to the state-of-the-art algorithms such as the variational method, and report many orders of magnitude gain in the execution time. The tensor method is also applicable for unsupervised learning of a wide range of latent variable models, and we also demonstrate efficient recovery of topics from the Nytimes dataset.

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"
    - "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"
    - "efficient active algorithms for hierarchical clustering"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "searching for a single community in a graph"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "estimating the number of communities in a network"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "exact recovery in the stochastic block model"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "breaking the small cluster barrier of graph clustering"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (induced by source paper "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "learning from multiway data simple and efficient tensor regression"
    - "polynomial time tensor decompositions with sum of squares"
    - "reinforcement learning of pomdps using spectral methods"
    - "tensorly tensor learning in python"
    - "tensor factorization via matrix factorization"
    - "learning mixtures of gaussians in high dimensions"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"

Connected through hyperedge HE-4 (induced by source paper "streaming graph challenge stochastic block partition"):
  with members:
    - "static graph challenge subgraph isomorphism"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "a high performance implementation of spectral clustering on cpu gpu platforms"
    - "an incremental local first community detection method for dynamic graphs"
    - "bayesian discovery of threat networks"
    - "network sampling from static to streaming graphs"
    - "efficient and principled method for detecting communities in networks"

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 "spectral learning for supervised topic models"):
  with members:
    - "analyzing tensor power method dynamics in overcomplete regime"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "spectral experts for estimating mixtures of linear regressions"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-7 (induced by source paper "multilinear low rank tensors on graphs applications"):
  with members:
    - "fast robust pca on graphs"
    - "fast eigenspace approximation using random signals"
    - "compressive pca for low rank matrices on graphs"
    - "stationary time vertex signal processing"
    - "convolutional neural networks with low rank regularization"
    - "robust principal component analysis on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "tensor spectral clustering for partitioning higher order network structures"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "using triangles to improve community detection in directed networks"
    - "clustering and community detection in directed networks a survey"
    - "tensor decompositions for learning latent variable models"
    - "shaping communities out of triangles"
    - "degree relations of triangles in real world networks and graph models"
    - "mutual or unrequited love identifying stable clusters in social networks with uni and bi directional links"
    - "most tensor problems are np hard"

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)