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: "all in one multi task learning for rumour verification"
  Abstract: Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used.

Connected through hyperedge HE-1 (induced by source paper "two path deep semi supervised learning for timely fake news detection"):
  with members:
    - "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"
    - "an overview of multi task learning in deep neural networks"
    - "csi a hybrid deep model for fake news detection"
    - "very deep convolutional networks for text classification"
    - "temporal ensembling for semi supervised learning"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"

Connected through hyperedge HE-2 (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"
    - "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"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-3 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "automated fact checking task formulations methods and future directions"

Connected through hyperedge HE-4 (induced by source paper "modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity"):
  with members:
    - "a semi supervised approach to message stance classification"
    - "stance classification for rumour analysis in twitter exploiting affective information and conversation structure"
    - "combating fake news a survey on identification and mitigation techniques"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "identifying and understanding user reactions to deceptive and trusted social news sources"
    - "cross lingual cross platform rumor verification pivoting on multimedia content"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"

Connected through hyperedge HE-5 (induced by source paper "different absorption from the same sharing sifted multi task learning for fake news detection"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "automatic stance detection using end to end memory networks"
    - "zero resource multilingual model transfer learning what to share"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "a survey on natural language processing for fake news detection"
    - "multi source cross lingual model transfer learning what to share"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised content based detection of misinformation via tensor embeddings"

Connected through hyperedge HE-6 (induced by source paper "contrastive language adaptation for cross lingual stance detection"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "adversarial classifier for imbalanced problems"
    - "automatic stance detection using end to end memory networks"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fact checking in community forums"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-7 (induced by source paper "rumour detection via news propagation dynamics and user representation learning"):
  with members:
    - "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"
    - "fake news detection on social media a data mining perspective"

Connected through hyperedge HE-8 (induced by source paper "the limitations of stylometry for detecting machine generated fake news"):
  with members:
    - "real or fake learning to discriminate machine from human generated text"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "the language of legal and illegal activity on the darknet"
    - "the fact extraction and verification fever shared task"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "fake news detection in social networks via crowd signals"

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)