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: "ti cnn convolutional neural networks for fake news detection"
  Abstract: With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.

Connected through hyperedge HE-1 (induced by source paper "safe similarity aware multi modal fake news detection"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "different spirals of sameness a study of content sharing in mainstream and alternative media"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake news a survey of research detection methods and opportunities"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "network based fake news detection a pattern driven approach"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "fake news a survey of research detection methods and opportunities"
    - "a survey on natural language processing for fake news detection"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "the spread of low credibility content by social bots"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "automatic detection of fake news"

Connected through hyperedge HE-5 (induced by source paper "fake news detection using deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recent trends in deep learning based natural language processing"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-6 (induced by source paper "fake news detection using deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "fake news detection on social media using geometric deep learning"
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-7 (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"
    - "clickbait detection in tweets using self attentive network"
    - "relation extraction a survey"

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)