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: "call attention to rumors deep attention based recurrent neural networks for early rumor detection"
  Abstract: The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as \textit{early rumor detection}, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classification algorithms to rumor detection in earliness since they rely on hand-crafted features which require intensive manual efforts in the case of large amount of posts. This paper presents a deep attention model on the basis of recurrent neural networks (RNN) to learn \textit{selectively} temporal hidden representations of sequential posts for identifying rumors. The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep attention based RNN model outperforms state-of-the-arts that rely on hand-crafted features; (2) the introduction of soft attention mechanism can effectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.

Connected through hyperedge HE-1 (induced by source paper "combating fake news a survey on identification and mitigation techniques"):
  with members:
    - "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"
    - "adversarial classification on social networks"
    - "all in one multi task learning for rumour verification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fever a large scale dataset for fact extraction and verification"
    - "detection and resolution of rumours in social media a survey"
    - "me my echo chamber and i introspection on social media polarization"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-2 (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"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "fake news detection on social media a data mining perspective"

Connected through hyperedge HE-3 (induced by source paper "attention based neural architecture for rumor detection with author context awareness"):
  with members:
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "quantifying mental health from social media with neural user embeddings"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "csi a hybrid deep model for fake news detection"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "applications of online deep learning for crisis response using social media information"
    - "stance detection with bidirectional conditional encoding"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "fake news a survey of research detection methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "linknbed multi graph representation learning with entity linkage"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "belittling the source trustworthiness indicators to obfuscate fake news on the web"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "clickbait detection in tweets using self attentive network"

Connected through hyperedge HE-5 (induced by source paper "all in one multi task learning for rumour verification"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fake news detection on social media a data mining perspective"
    - "a multi task approach for named entity recognition in social media data"
    - "determining the veracity of rumours on twitter"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "automatic rumor detection on microblogs a survey"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "on early stage debunking rumors on twitter leveraging the wisdom of weak learners"
    - "csi a hybrid deep model for fake news detection"
    - "determining the veracity of rumours on twitter"
    - "image credibility analysis with effective domain transferred deep networks"
    - "tweetcred real time credibility assessment of content on twitter"

Connected through hyperedge HE-7 (induced by source paper "on the benefit of combining neural statistical and external features for fake news identification"):
  with members:
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "applying deep learning to answer selection a study and an open task"
    - "skip thought vectors"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "lstm based deep learning models for non factoid answer selection"
    - "deep learning for answer sentence selection"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "on early stage debunking rumors on twitter leveraging the wisdom of weak learners"):
  with members:
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "tweet2vec character based distributed representations for social media"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "bag of tricks for efficient text classification"
    - "a c lstm neural network for text classification"
    - "tweetcred real time credibility assessment of content on twitter"
    - "convolutional neural networks for sentence classification"

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)