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: "correlated non parametric latent feature models"
  Abstract: We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised image annotation and segmentation with objects and attributes"):
  with members:
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "weakly supervised learning of objects attributes and their associations"
    - "vqa visual question answering"
    - "natural language object retrieval"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "deep visual semantic alignments for generating image descriptions"
    - "fully convolutional multi class multiple instance learning"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-2 (induced by source paper "role discovery in networks"):
  with members:
    - "a boosting approach to learning graph representations"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "does a daily deal promotion signal a distressed business an empirical investigation of small business survival"
    - "role dynamics fast mining of large dynamic networks"
    - "network sampling from static to streaming graphs"
    - "on learning discrete graphical models using greedy methods"
    - "the internet as level topology three data sources and one definitive metric"

Connected through hyperedge HE-3 (induced by source paper "indian buffet process deep generative models"):
  with members:
    - "semi supervised learning with deep generative models"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "the supervised ibp neighbourhood preserving infinite latent feature models"):
  with members:
    - "the phylogenetic indian buffet process a non exchangeable nonparametric prior for latent features"
    - "modeling images using transformed indian buffet processes"

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)