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 "harnessing the richness of the linguistic signal in predicting pragmatic inferences"):
  Abstract: The strength of pragmatic inferences systematically depends on linguistic and contextual cues. For example, the presence of a partitive construction increases the strength of a so-called scalar inference: humans perceive the inference that Chris did not eat all of the cookies to be stronger after hearing "Chris ate some of the cookies" than after hearing the same utterance without a partitive, "Chris ate some cookies". In this work, we explore to what extent it is possible to learn associations between linguistic cues and inference strength ratings without direct supervision. We show that an LSTM-based sentence encoder with an attention mechanism trained on a dataset of human inference strength ratings is able to predict ratings with high accuracy (r=0.78). We probe the model's behavior in multiple analyses using corpus data and manually constructed minimal pairs and find that the model learns associations between linguistic cues and scalar inferences, suggesting that these associations are inferable from statistical input.

Includes members:
  Member "attention is not not explanation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding image captioning models beyond visualizing attention")
  Member "what syntactic structures block dependencies in rnn language models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical representation in neural language models suppression and recovery of expectations")
  Member "attention is not explanation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "huggingface s transformers state of the art natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised question decomposition for question answering")
  Member "attention interpretability across nlp tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models")
  Member "neural language models as psycholinguistic subjects representations of syntactic state":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models")
  Member "linguistic knowledge and transferability of contextual representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "allennlp a deep semantic natural language processing platform":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")

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)