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: "bibliographic analysis with the citation network topic model"
  Abstract: Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and a clustering task compared to several baselines.

Connected through hyperedge HE-1 (induced by source paper "attributed social network embedding"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "birank towards ranking on bipartite graphs"
    - "wide deep learning for recommender systems"
    - "node2vec scalable feature learning for networks"
    - "latent space model for multi modal social data"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a survey on network embedding"):
  with members:
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "heterogeneous information network embedding for meta path based proximity"
    - "higher order organization of complex networks"
    - "a general framework for content enhanced network representation learning"
    - "end to end joint learning of natural language understanding and dialogue manager"
    - "deepwalk online learning of social representations"
    - "graph based anomaly detection and description a survey"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "scalable dynamic topic modeling with clustered latent dirichlet allocation clda"):
  with members:
    - "moliere automatic biomedical hypothesis generation system"
    - "peacock learning long tail topic features for industrial applications"
    - "dependent hierarchical normalized random measures for dynamic topic modeling"
    - "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream"

Connected through hyperedge HE-4 (induced by source paper "bibliographic analysis on research publications using authors categorical labels and the citation network"):
  with members:
    - "twitter network topic model a full bayesian treatment for social network and text modeling"
    - "scalable text and link analysis with mixed topic link models"
    - "the author topic model for authors and documents"

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.DL(Digital Libraries)