human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "mitre at semeval 2016 task 6 transfer learning for stance detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains")
  Member "bridging social media via distant supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rapid classification of crisis related data on social networks using convolutional neural networks")
  Member "identifying users with opposing opinions in twitter debates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "nrc canada building the state of the art in sentiment analysis of tweets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "a survey of paraphrasing and textual entailment methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visuallly grounded generation of entailments from premises")
  Member "get out the vote determining support or opposition from congressional floor debate transcripts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences")

Question: Please tell me which class the source paper 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)