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: "exploring the syntactic abilities of rnns with multi task learning"
  Abstract: Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure. RNNs performed this task well in common cases, but faltered in complex sentences (Linzen et al., 2016). We test whether these errors are due to inherent limitations of the architecture or to the relatively indirect supervision provided by most agreement dependencies in a corpus. We trained a single RNN to perform both the agreement task and an additional task, either CCG supertagging or language modeling. Multi-task training led to significantly lower error rates, in particular on complex sentences, suggesting that RNNs have the ability to evolve more sophisticated syntactic representations than shown before. We also show that easily available agreement training data can improve performance on other syntactic tasks, in particular when only a limited amount of training data is available for those tasks. The multi-task paradigm can also be leveraged to inject grammatical knowledge into language models.

Connected through hyperedge HE-1 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks"):
  with members:
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "a critical analysis of biased parsers in unsupervised parsing"
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "tree to tree neural networks for program translation"

Connected through hyperedge HE-2 (induced by source paper "assessing the memory ability of recurrent neural networks"):
  with members:
    - "learning with interpretable structure from rnn"
    - "evaluating the ability of lstms to learn context free grammars"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "language modeling with gated convolutional networks"
    - "using the output embedding to improve language models"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "character aware neural language models"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "assessing bert s syntactic abilities"
    - "universal transformers"
    - "finding syntax in human encephalography with beam search"
    - "distilling knowledge for search based structured prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "what should do can lstms learn when parsing auxiliary verb constructions"):
  with members:
    - "recursive subtree composition in lstm based dependency parsing"
    - "studying the inductive biases of rnns with synthetic variations of natural languages"
    - "can lstm learn to capture agreement the case of basque"
    - "deep contextualized word representations"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"

Connected through hyperedge HE-5 (induced by source paper "using priming to uncover the organization of syntactic representations in neural language models"):
  with members:
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "the emergence of number and syntax units in lstm language models"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "analysis methods in neural language processing a survey"

Connected through hyperedge HE-6 (induced by source paper "quantity doesn t buy quality syntax with neural language models"):
  with members:
    - "assessing bert s syntactic abilities"
    - "cloze driven pretraining of self attention networks"
    - "does bert agree evaluating knowledge of structure dependence through agreement relations"
    - "transformer xl attentive language models beyond a fixed length context"
    - "structural supervision improves learning of non local grammatical dependencies"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "neural arithmetic logic units"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "studying the inductive biases of rnns with synthetic variations of natural languages"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "what do rnn language models learn about filler gap dependencies"
    - "assessing composition in sentence vector representations"
    - "rnns as psycholinguistic subjects syntactic state and grammatical dependency"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "are all languages equally hard to language model"

Connected through hyperedge HE-8 (induced by source paper "neural language models as psycholinguistic subjects representations of syntactic state"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "effective inference for generative neural parsing"

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)