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: "semantic tagging with deep residual networks"
  Abstract: We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71% accuracy on UD v1.2 and 95.67% accuracy on UD v1.3).

Connected through hyperedge HE-1 (induced by source paper "parsing thai social data a new challenge for thai nlp"):
  with members:
    - "a simple lstm model for transition based dependency parsing"
    - "deep biaffine attention for neural dependency parsing"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "a universal part of speech tagset"

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"
    - "the emergence of number and syntax units in lstm language models"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "shallow syntax in deep water"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "comprehensive supersense disambiguation of english prepositions and possessives"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "neural models of factuality"

Connected through hyperedge HE-5 (induced by source paper "transductive auxiliary task self training for neural multi task models"):
  with members:
    - "choosing transfer languages for cross lingual learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural amr sequence to sequence models for parsing and generation"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "one model to rule them all multitask and multilingual modelling for lexical analysis"
    - "learning to select data for transfer learning with bayesian optimization"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-6 (induced by source paper "multi task learning for sequence tagging an empirical study"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "scheduled multi task learning from syntax to translation"
    - "learning to parse and translate improves neural machine translation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "bootstrapping generators from noisy data"):
  with members:
    - "handling divergent reference texts when evaluating table to text generation"
    - "sentence simplification with deep reinforcement learning"
    - "what does attention in neural machine translation pay attention to"
    - "learning to generate one sentence biographies from wikidata"
    - "order planning neural text generation from structured data"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-8 (induced by source paper "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"):
  with members:
    - "revisiting the importance of individual units in cnns via ablation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "identifying and controlling important neurons in neural machine translation"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "neurox a toolkit for analyzing individual neurons in neural networks"
    - "attention is all you need"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"

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)