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: "a simple but tough to beat baseline for the fake news challenge stance detection task"
  Abstract: Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.

Connected through hyperedge HE-1 (induced by source paper "towards quantifying the distance between opinions"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "viewpoint discovery and understanding in social networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "globally normalized transition based neural networks"
    - "distributed representations of sentences and documents"
    - "opinion mining and analysis a survey"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (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"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-3 (induced by source paper "false news on social media a data driven survey"):
  with members:
    - "the junk news aggregator examining junk news posted on facebook starting with the 2018 us midterm elections"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "automated fact checking task formulations methods and future directions"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "anatomy of an online misinformation network"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "detection and resolution of rumours in social media a survey"

Connected through hyperedge HE-4 (induced by source paper "taking a stance on fake news towards automatic disinformation assessment via deep bidirectional transformer language models for stance detection"):
  with members:
    - "assessing social and intersectional biases in contextualized word representations"
    - "fakta an automatic end to end fact checking system"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "measuring bias in contextualized word representations"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a retrospective analysis of the fake news challenge stance detection task"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (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-7 (induced by source paper "transfer learning from transformers to fake news challenge stance detection fnc 1 task"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "a simple method for commonsense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-8 (induced by source paper "adversarial domain adaptation for stance detection"):
  with members:
    - "hunting for troll comments in news community forums"
    - "fever a large scale dataset for fact extraction and verification"
    - "fact checking in community forums"
    - "fully automated fact checking using external sources"
    - "a large annotated corpus for learning natural language inference"
    - "deep learning for answer sentence selection"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

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)