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: "learning reporting dynamics during breaking news for rumour detection in social media"
  Abstract: Breaking news leads to situations of fast-paced reporting in social media, producing all kinds of updates related to news stories, albeit with the caveat that some of those early updates tend to be rumours, i.e., information with an unverified status at the time of posting. Flagging information that is unverified can be helpful to avoid the spread of information that may turn out to be false. Detection of rumours can also feed a rumour tracking system that ultimately determines their veracity. In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories. Using Twitter datasets collected during five breaking news stories, we experiment with Conditional Random Fields as a sequential classifier that leverages context learnt during an event for rumour detection, which we compare with the state-of-the-art rumour detection system as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying a piece of information to deem it a rumour, but instead we detect rumours from the tweet alone by exploiting context learnt during the event. Our classifier achieves competitive performance, beating the state-of-the-art classifier that relies on querying tweets with improved precision and recall, as well as outperforming our best baseline with nearly 40% improvement in terms of F1 score. The scale and diversity of our experiments reinforces the generalisability of our classifier.

Connected through hyperedge HE-1 (induced by source paper "rumour detection via news propagation dynamics and user representation learning"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "detection and resolution of rumours in social media a survey"
    - "ti cnn convolutional neural networks for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "multiview deep learning for predicting twitter users location"
    - "call attention to rumors deep attention based recurrent neural networks for early rumor detection"
    - "a stylometric inquiry into hyperpartisan and fake news"

Connected through hyperedge HE-2 (induced by source paper "neural language model based training data augmentation for weakly supervised early rumor detection"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep contextualized word representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "a longitudinal assessment of the persistence of twitter datasets"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "deepstance at semeval 2016 task 6 detecting stance in tweets using character and word level cnns"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "deep two path semi supervised learning for fake news detection"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "a survey on natural language processing for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "temporal ensembling for semi supervised learning"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "emotion cognizance improves fake news identification"):
  with members:
    - "word affect intensities"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "emotion cognizance improves health fake news identification"):
  with members:
    - "exploiting emotions for fake news detection on social media"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "joint modeling of content and discourse relations in dialogues"
    - "jointly learning word embeddings and latent topics"
    - "tweet2vec character based distributed representations for social media"
    - "skip thought vectors"
    - "unsupervised cross domain word representation learning"
    - "distributed representations of sentences and documents"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"

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)