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: "the emergence of number and syntax units in lstm language models"
  Abstract: Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this remarkable feat. Some have conjectured it depends on heuristics that do not truly take hierarchical structure into account. We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two ``number units''. Importantly, the behaviour of these units is partially controlled by other units independently shown to track syntactic structure. We conclude that LSTMs are, to some extent, implementing genuinely syntactic processing mechanisms, paving the way to a more general understanding of grammatical encoding in LSTMs.

Connected through hyperedge HE-1 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "measuring compositionality in representation learning"
    - "what do rnn language models learn about filler gap dependencies"
    - "on adversarial examples for character level neural machine translation"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "jump to better conclusions scan both left and right"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"

Connected through hyperedge HE-3 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "open sesame getting inside bert s linguistic knowledge"

Connected through hyperedge HE-4 (induced by source paper "evaluating recurrent neural network explanations"):
  with members:
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "hierarchical interpretations for neural network predictions"
    - "explaining character aware neural networks for word level prediction do they discover linguistic rules"
    - "understanding convolutional neural networks for text classification"
    - "methods for interpreting and understanding deep neural networks"
    - "rudder return decomposition for delayed rewards"
    - "an empirical analysis of the role of amplifiers downtoners and negations in emotion classification in microblogs"

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"
    - "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"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"

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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "lstm sharp an adaptable energy efficient hardware accelerator for long short term memory"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "recent advances in recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bidirectional attention flow for machine comprehension"
    - "optimizing performance of recurrent neural networks on gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-8 (induced by source paper "analysing neural language models contextual decomposition reveals default reasoning in number and gender assignment"):
  with members:
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "hierarchical interpretations for neural network predictions"
    - "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"
    - "targeted syntactic evaluation of language models"

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)