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: "stance detection with bidirectional conditional encoding"
  Abstract: Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral". Previous work has assumed that either the target is mentioned in the text or that training data for every target is given. This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets. We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms encoding the tweet and the target independently. Performance is improved further when the conditional model is augmented with bidirectional encoding. We evaluate our approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best only to a system trained on semi-automatically labelled tweets for the test target. When such weak supervision is added, our approach achieves state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains"):
  with members:
    - "recognising agreement and disagreement between stances with reason comparing networks"
    - "domain adaptation with adversarial training and graph embeddings"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "deep memory networks for attitude identification"
    - "deep coral correlation alignment for deep domain adaptation"
    - "bag of tricks for efficient text classification"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"

Connected through hyperedge HE-2 (induced by source paper "x stance a multilingual multi target dataset for stance detection"):
  with members:
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-3 (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"
    - "all in one multi task learning for rumour verification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multifc a real world multi domain dataset for evidence based fact checking of claims"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "end to end neural entity linking"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "twowingos a two wing optimization strategy for evidential claim verification"

Connected through hyperedge HE-5 (induced by source paper "danish stance classification and rumour resolution"):
  with members:
    - "fake news detection using stance classification a survey"
    - "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers"
    - "clearumor at semeval 2019 task 7 convolving elmo against rumors"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the fact extraction and verification fever shared task"
    - "detection and resolution of rumours in social media a survey"
    - "learning word vectors for 157 languages"
    - "rumoureval 2019 determining rumour veracity and support for rumours"

Connected through hyperedge HE-6 (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"
    - "automatic stance detection using end to end memory networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "recognising agreement and disagreement between stances with reason comparing networks"):
  with members:
    - "attention is all you need"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "bilateral multi perspective matching for natural language sentences"
    - "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "stance and sentiment in tweets"
    - "context dependent translation selection using convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "mapping dis information flow about the mh17 plane crash"):
  with members:
    - "universal dependency parsing from scratch"
    - "issue framing in online discussion fora"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "junk news on military affairs and national security social media disinformation campaigns against us military personnel and veterans"
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "neural discourse structure for text categorization"
    - "tweet2vec character based distributed representations for social media"

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)