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: "deep rnns encode soft hierarchical syntax"
  Abstract: We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it. These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling. In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges. This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  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"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (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"
    - "the emergence of number and syntax units in lstm language models"

Connected through hyperedge HE-3 (induced by source paper "assessing the ability of self attention networks to learn word order"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "modeling recurrence for transformer"
    - "towards robust neural machine translation"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "what do you learn from context probing for sentence structure in contextualized word representations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "neural davidsonian semantic proto role labeling"
    - "assessing composition in sentence vector representations"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "jointly predicting predicates and arguments in neural semantic role labeling"

Connected through hyperedge HE-7 (induced by source paper "discovering the compositional structure of vector representations with role learning networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "correlating neural and symbolic representations of language"
    - "measuring compositionality in representation learning"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "analyzing machine learned representations a natural language case study"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-8 (induced by source paper "what does bert look at an analysis of bert s attention"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "attention is not explanation"
    - "linguistic knowledge and transferability of contextual representations"
    - "visualizing attention in transformer based language representation 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)