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: "constituent parsing as sequence labeling"
  Abstract: We introduce a method to reduce constituent parsing to sequence labeling. For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.

Connected through hyperedge HE-1 (induced by source paper "parsing as pretraining"):
  with members:
    - "assessing bert s syntactic abilities"
    - "viable dependency parsing as sequence labeling"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "better faster stronger sequence tagging constituent parsers"):
  with members:
    - "multilingual constituency parsing with self attention and pre training"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"
    - "policy gradient as a proxy for dynamic oracles in constituency parsing"

Connected through hyperedge HE-3 (induced by source paper "sequence labeling parsing by learning across representations"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "left to right dependency parsing with pointer networks"
    - "multitask parsing across semantic representations"
    - "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 "how to best use syntax in semantic role labelling"):
  with members:
    - "syntax aware neural semantic role labeling"
    - "syntactic scaffolds for semantic structures"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "multi task learning for sequence tagging an empirical study"
    - "evaluating the utility of hand crafted features in sequence labelling"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-5 (induced by source paper "tetra tagging word synchronous parsing with linear time inference"):
  with members:
    - "better faster stronger sequence tagging constituent parsers"
    - "multilingual constituency parsing with self attention and pre training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "recurrent neural network grammars"
    - "global neural ccg parsing with optimality guarantees"
    - "neural crf parsing"

Connected through hyperedge HE-6 (induced by source paper "viable dependency parsing as sequence labeling"):
  with members:
    - "scheduled multi task learning from syntax to translation"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "sequence to sequence learning as beam search optimization"
    - "dependency parsing as head selection"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"
    - "grammar as a foreign language"

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)