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 "decoding sentiment from distributed representations of sentences"):
  Abstract: Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no `one-size-fits-all' representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language and data at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bidirectional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.

Includes members:
  Member "structural attention neural networks for improved sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a framework for end to end learning on semantic tree structured data")
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "adversarial deep averaging networks for cross lingual sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "calibration of pre trained transformers")
  Member "learning distributed representations of sentences from unlabelled data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual lensing of universal sentence representations")
  Member "charagram embedding words and sentences via character n grams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")
  Member "visualizing and understanding neural models in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")
  Member "skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "towards universal paraphrastic sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")

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)