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: "syntactic topic models"
  Abstract: The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents. It assumes that each word is drawn from a latent topic chosen by combining document-level features and the local syntactic context. Each document has a distribution over latent topics, as in topic models, which provides the semantic consistency. Each element in the dependency parse tree also has a distribution over the topics of its children, as in latent-state syntax models, which provides the syntactic consistency. These distributions are convolved so that the topic of each word is likely under both its document and syntactic context. We derive a fast posterior inference algorithm based on variational methods. We report qualitative and quantitative studies on both synthetic data and hand-parsed documents. We show that the STM is a more predictive model of language than current models based only on syntax or only on topics.

Connected through hyperedge HE-1 (induced by source paper "automatic evaluation of local topic quality"):
  with members:
    - "on a topic model for sentences"
    - "a practical algorithm for topic modeling with provable guarantees"

Connected through hyperedge HE-2 (induced by source paper "on a topic model for sentences"):
  with members:
    - "an adaptation of topic modeling to sentences"
    - "lshtc a benchmark for large scale text classification"

Connected through hyperedge HE-3 (induced by source paper "word representations tree models and syntactic functions"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "factorial hidden markov models for learning representations of natural language"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "joint image text news topic detection and tracking with and or graph representation"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "integrating document clustering and topic modeling"

Connected through hyperedge HE-5 (induced by source paper "tag weighted topic model for large scale semi structured documents"):
  with members:
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"
    - "weighted naive bayes model for semi structured document categorization"

Connected through hyperedge HE-6 (induced by source paper "learning frames from text with an unsupervised latent variable model"):
  with members:
    - "probabilistic frame induction"
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"
    - "a reliable effective terascale linear learning system"
    - "inducing a semantically annotated lexicon via em based clustering"

Connected through hyperedge HE-7 (induced by source paper "maps of computer science"):
  with members:
    - "putting recommendations on the map visualizing clusters and relations"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "mapping topics and topic bursts in pnas"

Connected through hyperedge HE-8 (induced by source paper "topic segmentation and labeling in asynchronous conversations"):
  with members:
    - "catching the drift probabilistic content models with applications to generation and summarization"
    - "learning algorithms for keyphrase extraction"

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.CL(Computation and Language)