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 and sentiment in tweets"
  Abstract: We can often detect from a person's utterances whether he/she is in favor of or against a given target entity -- their stance towards the target. However, a person may express the same stance towards a target by using negative or positive language. Here for the first time we present a dataset of tweet--target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that while knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.

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"
    - "stance detection with bidirectional conditional encoding"
    - "deep coral correlation alignment for deep domain adaptation"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "a semi supervised approach to message stance classification"):
  with members:
    - "rumor detection and classification for twitter data"
    - "detection and resolution of rumours in social media a survey"
    - "fake news detection on social media a data mining perspective"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "determining the veracity of rumours on twitter"
    - "using gaussian processes for rumour stance classification in social media"
    - "a tutorial on online supervised learning with applications to node classification in social networks"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"

Connected through hyperedge HE-3 (induced by source paper "multi agent reinforcement learning and human social factors in climate change mitigation"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "cooperating with machines"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "affect control processes"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-4 (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-5 (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-6 (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 detection with bidirectional conditional encoding"
    - "context dependent translation selection using convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "towards multimodal emotion recognition in german speech events in cars using transfer learning"):
  with members:
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "learning word vectors for 157 languages"
    - "iest wassa 2018 implicit emotions shared task"
    - "amobee at iest 2018 transfer learning from language models"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "emotion intensities in tweets"
    - "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech"
    - "distant supervision for emotion detection using facebook reactions"

Connected through hyperedge HE-8 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators"):
  with members:
    - "predicting factuality of reporting and bias of news media sources"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "quootstrap scalable unsupervised extraction of quotation speaker pairs from large news corpora via bootstrapping"
    - "fake news detection on social media a data mining perspective"
    - "learning to identify ambiguous and misleading news headlines"
    - "hoaxy a platform for tracking online misinformation"
    - "computational fact checking from knowledge networks"
    - "characterizing the life cycle of online news stories using social media reactions"

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)