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: "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
  Abstract: Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.

Connected through hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

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 "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-5 (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-6 (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"
    - "automatic stance detection using end to end memory networks"

Connected through hyperedge HE-7 (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"
    - "automatic stance detection using end to end memory networks"
    - "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"

Connected through hyperedge HE-8 (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"

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)