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: "discriminative relational topic models"
  Abstract: Many scientific and engineering fields involve analyzing network data. For document networks, relational topic models (RTMs) provide a probabilistic generative process to describe both the link structure and document contents, and they have shown promise on predicting network structures and discovering latent topic representations. However, existing RTMs have limitations in both the restricted model expressiveness and incapability of dealing with imbalanced network data. To expand the scope and improve the inference accuracy of RTMs, this paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference (RegBayes) with a regularization parameter to deal with the imbalanced link structure issue in common real networks and improve the discriminative ability of learned latent representations; and 3) instead of doing variational approximation with strict mean-field assumptions, we present collapsed Gibbs sampling algorithms for the generalized relational topic models by exploring data augmentation without making restricting assumptions. Under the generic RegBayes framework, we carefully investigate two popular discriminative loss functions, namely, the logistic log-loss and the max-margin hinge loss. Experimental results on several real network datasets demonstrate the significance of these extensions on improving the prediction performance, and the time efficiency can be dramatically improved with a simple fast approximation method.

Connected through hyperedge HE-1 (induced by source paper "saberlda sparsity aware learning of topic models on gpus"):
  with members:
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "big learning with bayesian methods"
    - "same but different fast and high quality gibbs parameter estimation"

Connected through hyperedge HE-2 (induced by source paper "max margin nonparametric latent feature models for link prediction"):
  with members:
    - "discriminative nonparametric latent feature relational models with data augmentation"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "discriminative nonparametric latent feature relational models with data augmentation"):
  with members:
    - "max margin nonparametric latent feature models for link prediction"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "online bayesian passive aggressive learning"):
  with members:
    - "max margin nonparametric latent feature models for link prediction"
    - "expectation propagation for approximate bayesian inference"
    - "austerity in mcmc land cutting the metropolis hastings budget"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"
    - "sparse stochastic inference for latent dirichlet allocation"

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)