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: "classifying tweet level judgements of rumours in social media"
  Abstract: Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We formulate the problem of classifying tweet level judgements of rumours as a supervised learning task. Both supervised and unsupervised domain adaptation are considered, in which tweets from a rumour are classified on the basis of other annotated rumours. We demonstrate how multi-task learning helps achieve good results on rumours from the 2011 England riots.

Connected through hyperedge HE-1 (induced by source paper "rp dnn a tweet level propagation context based deep neural networks for early rumor detection in social media"):
  with members:
    - "attention based neural architecture for rumor detection with author context awareness"
    - "neural language model based training data augmentation for weakly supervised early rumor detection"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "detection and resolution of rumours in social media a survey"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "attention is all you need"
    - "csi a hybrid deep model for fake news detection"

Connected through hyperedge HE-2 (induced by source paper "a semi supervised approach to message stance classification"):
  with members:
    - "rumor detection and classification for twitter data"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "detection and resolution of rumours in social media a survey"):
  with members:
    - "rumor detection and classification for twitter data"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "discourse aware rumour stance classification in social media using sequential classifiers"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "using gaussian processes for rumour stance classification in social media"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "stance detection with bidirectional conditional encoding"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"

Connected through hyperedge HE-5 (induced by source paper "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "hoaxy a platform for tracking online misinformation"
    - "towards detecting rumours in social media"
    - "tweetcred real time credibility assessment of content on twitter"

Connected through hyperedge HE-6 (induced by source paper "using gaussian processes for rumour stance classification in social media"):
  with members:
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "microblog analysis as a programme of work"
    - "efficient estimation of word representations in vector space"
    - "expectation propogation for the generative aspect model"

Connected through hyperedge HE-7 (induced by source paper "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"):
  with members:
    - "using gaussian processes for rumour stance classification in social media"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "microblog analysis as a programme of work"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "analysing how people orient to and spread rumours in social media by looking at conversational threads"):
  with members:
    - "microblog analysis as a programme of work"
    - "towards detecting rumours in social media"
    - "tweet but verify epistemic study of information verification on twitter"

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)