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: "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
  Abstract: Syntactic rules in human language usually refer to the hierarchical structure of sentences. However, the input during language acquisition can often be explained equally well with rules based on linear order. The fact that children consistently ignore these linear explanations to instead settle on hierarchical explanations has been used to argue for an innate hierarchical bias in humans. We revisit this argument by using recurrent neural networks (RNNs), which have no hierarchical bias, to simulate the acquisition of question formation, a hierarchical transformation, in an artificial language modeled after English. Even though this transformation could be explained with a linear rule, we find that some RNN architectures consistently learn the correct hierarchical rule instead. This finding suggests that hierarchical cues within the language are sufficient to induce a preference for hierarchical generalization. This conclusion is strengthened by the finding that adding an additional hierarchical cue, namely syntactic agreement, further improves performance.

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"
    - "tree to tree neural networks for program translation"
    - "rnns implicitly implement tensor product representations"

Connected through hyperedge HE-2 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"):
  with members:
    - "assessing bert s syntactic abilities"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "assessing composition in sentence vector representations"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "teaching syntax by adversarial distraction"

Connected through hyperedge HE-3 (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"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "learning to generate reviews and discovering sentiment"
    - "are all languages equally hard to language model"

Connected through hyperedge HE-4 (induced by source paper "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "correlating neural and symbolic representations of language"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"

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 "open sesame getting inside bert s linguistic knowledge"):
  with members:
    - "fine tune bert for extractive summarization"
    - "assessing bert s syntactic abilities"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "deep contextualized word representations"
    - "context aware neural machine translation learns anaphora resolution"

Connected through hyperedge HE-7 (induced by source paper "does bert agree evaluating knowledge of structure dependence through agreement relations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "what does bert look at an analysis of bert s attention"
    - "marrying universal dependencies and universal morphology"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "hierarchical representation in neural language models suppression and recovery of expectations"):
  with members:
    - "what syntactic structures block dependencies in rnn language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "structural supervision improves learning of non local grammatical dependencies"
    - "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"
    - "rnns as psycholinguistic subjects syntactic state and grammatical dependency"
    - "on the practical computational power of finite precision rnns for language recognition"

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)