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: "latent topic models for hypertext"
  Abstract: Latent topic models have been successfully applied as an unsupervised topic discovery technique in large document collections. With the proliferation of hypertext document collection such as the Internet, there has also been great interest in extending these approaches to hypertext [6, 9]. These approaches typically model links in an analogous fashion to how they model words - the document-link co-occurrence matrix is modeled in the same way that the document-word co-occurrence matrix is modeled in standard topic models. In this paper we present a probabilistic generative model for hypertext document collections that explicitly models the generation of links. Specifically, links from a word w to a document d depend directly on how frequent the topic of w is in d, in addition to the in-degree of d. We show how to perform EM learning on this model efficiently. By not modeling links as analogous to words, we end up using far fewer free parameters and obtain better link prediction results.

Connected through hyperedge HE-1 (induced by source paper "hybrid clustering based on content and connection structure using joint nonnegative matrix factorization"):
  with members:
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-2 (induced by source paper "scalable models for computing hierarchies in information networks"):
  with members:
    - "deltacon a principled massive graph similarity function"
    - "the author topic model for authors and documents"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-3 (induced by source paper "discriminative relational topic models"):
  with members:
    - "max margin nonparametric latent feature models for link prediction"
    - "probabilistic latent semantic analysis"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"
    - "representation learning a review and new perspectives"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "scalable text and link analysis with mixed topic link models"):
  with members:
    - "a bayesian framework for community detection integrating content and link"
    - "latent multi group membership graph model"
    - "active learning for node classification in assortative and disassortative networks"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-5 (induced by source paper "a bayesian framework for community detection integrating content and link"):
  with members:
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"

Connected through hyperedge HE-6 (induced by source paper "higher order markov tag topic models for tagged documents and images"):
  with members:
    - "learning topic models by belief propagation"
    - "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.IR(Information Retrieval)