human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
  Abstract: Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter, Pinker & Prince (1988) presented a comprehensive rebuttal of many of Rumelhart and McClelland's claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland (1986) model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince's criticisms without requiring any simplication of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a re-examination of their utility in linguistic and cognitive modeling.

Connected through hyperedge HE-1 (induced by source paper "tabula nearly rasa probing the linguistic knowledge of character level neural language models trained on unsegmented text"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "indicatements that character language models learn english morpho syntactic units and regularities"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "assessing composition in sentence vector representations"
    - "an analysis of neural language modeling at multiple scales"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "learning to generate reviews and discovering sentiment"
    - "are all languages equally hard to language model"

Connected through hyperedge HE-2 (induced by source paper "acquisition of inflectional morphology in artificial neural networks with prior knowledge"):
  with members:
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "neural transductive learning and beyond morphological generation in the minimal resource setting"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "get to the point summarization with pointer generator networks"
    - "towards linguistically generalizable nlp systems a workshop and shared task"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-3 (induced by source paper "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"):
  with members:
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "jump to better conclusions scan both left and right"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "are we there yet encoder decoder neural networks as cognitive models of english past tense inflection"):
  with members:
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "opennmt open source toolkit for neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Question: Please tell me which class the center node 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)