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: "rumor detection and classification for twitter data"
  Abstract: With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.

Connected through hyperedge HE-1 (induced by source paper "a semi supervised approach to message stance classification"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "fake news detection on social media a data mining perspective"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "using gaussian processes for rumour stance classification in social media"
    - "a tutorial on online supervised learning with applications to node classification in social networks"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-2 (induced by source paper "quickstop a markov optimal stopping approach for quickest misinformation detection"):
  with members:
    - "fake news detection in social networks via crowd signals"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "the spread of fake news by social bots"
    - "fake news detection on social media a data mining perspective"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-3 (induced by source paper "detection and resolution of rumours in social media a survey"):
  with members:
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "simple open stance classification for rumour analysis"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "hoaxy a platform for tracking online misinformation"
    - "contradiction detection for rumorous claims"

Connected through hyperedge HE-4 (induced by source paper "how to become instagram famous post popularity prediction with dual attention"):
  with members:
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "time matters multi scale temporalization of social media popularity"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic coattention networks for question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"

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)