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 "neural network acceptability judgments"):
  Abstract: This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.'s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.

Includes members:
  Member "structural supervision improves learning of non local grammatical dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compound probabilistic context free grammars for grammar induction")
  Member "what do rnn language models learn about filler gap dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "assessing composition in sentence vector representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to learn words from visual scenes")
  Member "rnns as psycholinguistic subjects syntactic state and grammatical dependency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blimp a benchmark of linguistic minimal pairs for english")
  Member "glue a multi task benchmark and analysis platform for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "what you can cram into a single vector probing sentence embeddings for linguistic properties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "targeted syntactic evaluation of language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised recurrent neural network grammars")

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)