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: "viable dependency parsing as sequence labeling"
  Abstract: We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.

Connected through hyperedge HE-1 (induced by source paper "parsing as pretraining"):
  with members:
    - "assessing bert s syntactic abilities"
    - "better faster stronger sequence tagging constituent parsers"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "designing and interpreting probes with control tasks"
    - "the non utility of structural features in bilstm based dependency parsers"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "linguistic knowledge and transferability of contextual representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"

Connected through hyperedge HE-2 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "sequence labeling parsing by learning across representations"):
  with members:
    - "better faster stronger sequence tagging constituent parsers"
    - "left to right dependency parsing with pointer networks"
    - "multitask parsing across semantic representations"
    - "constituent parsing as sequence labeling"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "technical notes syntax aware representation learning with pointer networks"):
  with members:
    - "non projective dependency parsing via latent heads representation lhr"
    - "stack pointer networks for dependency parsing"
    - "scheduled multi task learning from syntax to translation"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "artificially evolved chunks for morphosyntactic analysis"):
  with members:
    - "better faster stronger sequence tagging constituent parsers"
    - "ncrf an open source neural sequence labeling toolkit"

Connected through hyperedge HE-6 (induced by source paper "towards making a dependency parser see"):
  with members:
    - "better faster stronger sequence tagging constituent parsers"

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)