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 "do rnns learn human like abstract word order preferences"):
  Abstract: RNN language models have achieved state-of-the-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN language models learn humanlike word order preferences in syntactic alternations. We collect language model surprisal scores for controlled sentence stimuli exhibiting major syntactic alternations in English: heavy NP shift, particle shift, the dative alternation, and the genitive alternation. We show that RNN language models reproduce human preferences in these alternations based on NP length, animacy, and definiteness. We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations. We show that the RNNs' performance is similar to the human acceptability ratings and is not matched by an n-gram baseline model. Our results show that RNNs learn the abstract features of weight, animacy, and definiteness which underlie soft constraints on syntactic alternations.

Includes members:
  Member "what can linguistics and deep learning contribute to each other":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  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 "rnns as psycholinguistic subjects syntactic state and grammatical dependency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blimp a benchmark of linguistic minimal pairs for english")
  Member "colorless green recurrent networks dream hierarchically":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "targeted syntactic evaluation of language models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "do latent tree learning models identify meaningful structure in sentences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "exploring the limits of language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)