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: "a practical algorithm for topic modeling with provable guarantees"
  Abstract: Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for topic model inference that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"):
  with members:
    - "everything old is new again a multi view learning approach to learning using privileged information and distillation"
    - "deep learning for sentiment analysis a survey"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "generalized separable nonnegative matrix factorization"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "learning the information divergence"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "crowdsourcing via pairwise co occurrences identifiability and algorithms"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "learning hidden markov models from pairwise co occurrences with applications to topic modeling"
    - "a tensor approach to learning mixed membership community models"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "a provably correct and robust algorithm for convolutive nonnegative matrix factorization"):
  with members:
    - "fast convolutive nonnegative matrix factorization through coordinate and block coordinate updates"
    - "multiplicative updates for elastic net regularized convolutional nmf under β divergence"
    - "learning topic models going beyond svd"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "a new evaluation framework for topic modeling algorithms based on synthetic corpora"):
  with members:
    - "provable algorithms for inference in topic models"
    - "integrating document clustering and topic modeling"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-6 (induced by source paper "a new anchor word selection method for the separable topic discovery"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "topic modeling via full dependence mixtures"):
  with members:
    - "adam a method for stochastic optimization"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-8 (induced by source paper "interactive topic modeling with anchor words"):
  with members:
    - "local algorithms for interactive clustering"
    - "learning topic models going beyond svd"

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)