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: "topic models conditioned on arbitrary features with dirichlet multinomial regression"
  Abstract: Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates. We show that by selecting appropriate features, DMR topic models can meet or exceed the performance of several previously published topic models designed for specific data.

Connected through hyperedge HE-1 (induced by source paper "homogeneity based transmissive process to model true and false news in social networks"):
  with members:
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "false information on web and social media a survey"
    - "limiting the spread of fake news on social media platforms by evaluating users trustworthiness"
    - "fake news detection in social networks via crowd signals"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "fake news mitigation via point process based intervention"
    - "joint modeling of topics citations and topical authority in academic corpora"
    - "science vs conspiracy collective narratives in the age of misinformation"

Connected through hyperedge HE-2 (induced by source paper "learning representations of social media users"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multi task learning for argumentation mining in low resource settings"
    - "semi supervised user geolocation via graph convolutional networks"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "deep generalized canonical correlation analysis"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"

Connected through hyperedge HE-3 (induced by source paper "a probabilistic model of the bitcoin blockchain"):
  with members:
    - "characterizing entities in the bitcoin blockchain"
    - "data mining for detecting bitcoin ponzi schemes"
    - "ransomware payments in the bitcoin ecosystem"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "blockchain a graph primer"
    - "the unreasonable effectiveness of address clustering"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "computer assisted text analysis for social science topic models and beyond"):
  with members:
    - "online visual analytics of text streams"
    - "efficient estimation of word representations in vector space"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-5 (induced by source paper "metalda a topic model that efficiently incorporates meta information"):
  with members:
    - "improving topic models with latent feature word representations"
    - "hierarchical dirichlet scaling process"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "demographic dialectal variation in social media a case study of african american english"):
  with members:
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-7 (induced by source paper "experience aware item recommendation in evolving review communities"):
  with members:
    - "exploiting social network structure for person to person sentiment analysis"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-8 (induced by source paper "tag weighted topic model for large scale semi structured documents"):
  with members:
    - "syntactic topic models"
    - "weighted naive bayes model for semi structured document categorization"

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)