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: "automatic stance detection using end to end memory networks"
  Abstract: We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction. The network operates at the paragraph level and integrates convolutional and recurrent neural networks, as well as a similarity matrix as part of the overall architecture. The experimental evaluation on the Fake News Challenge dataset shows state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "joint inference on truth rumor and their sources in social networks"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "necessary and sufficient budgets in information source finding with querying adaptivity gap"
    - "gemsec graph embedding with self clustering"
    - "on measuring bias in online information"
    - "fake news detection on social media a data mining perspective"
    - "rumor source detection under querying with untruthful answers"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-2 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "devam vs tamam 2018 turkish elections"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "bag of tricks for efficient text classification"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-3 (induced by source paper "memory attended recurrent network for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "video captioning via hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "cpgan full spectrum content parsing generative adversarial networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "memory attended recurrent network for video captioning"
    - "mirrorgan learning text to image generation by redescription"
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "yolov3 an incremental improvement"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "automatic fact checking using context and discourse information"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "claimrank detecting check worthy claims in arabic and english"
    - "integrating stance detection and fact checking in a unified corpus"

Connected through hyperedge HE-6 (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"
    - "all in one multi task learning for rumour verification"
    - "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-7 (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"
    - "all in one multi task learning for rumour verification"
    - "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-8 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "assessing sentiment of the expressed stance on social media"
    - "topical stance detection for twitter a two phase lstm model using attention"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "hiding individuals and communities in a social network"
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "reducing controversy by connecting opposing views"
    - "stance detection with bidirectional conditional encoding"

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)