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: "et lda joint topic modeling for aligning events and their twitter feedback"
  Abstract: During broadcast events such as the Superbowl, the U.S. Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the topics covered by the event and the tweets; the other is to segment the event. So far these problems have been viewed separately and studied in isolation. In this work, we argue that these problems are in fact inter-dependent and should be addressed together. We develop a joint Bayesian model that performs topic modeling and event segmentation in one unified framework. We evaluate the proposed model both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.

Connected through hyperedge HE-1 (induced by source paper "event detection and retrieval on social media"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "social event detection with interaction graph modeling"

Connected through hyperedge HE-2 (induced by source paper "latent dirichlet allocation lda and topic modeling models applications a survey"):
  with members:
    - "improving topic models with latent feature word representations"
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "fast online em for big topic modeling"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "unveiling the political agenda of the european parliament plenary a topical analysis"
    - "expectation propogation for the generative aspect model"
    - "the author topic model for authors and documents"
    - "modeling online reviews with multi grain topic models"

Connected through hyperedge HE-3 (induced by source paper "et lda joint topic modeling for aligning analyzing and sensemaking of public events and their twitter feeds"):
  with members:
    - "modeling online reviews with multi grain topic models"
    - "catching the drift probabilistic content models with applications to generation and summarization"

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.SI(Social and Information Networks)