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: "a full end to end semantic role labeler syntax agnostic over syntax aware"
  Abstract: Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into two or more subtasks. For the first time, this paper introduces an end-to-end neural model which unifiedly tackles the predicate disambiguation and the argument labeling in one shot. Using a biaffine scorer, our model directly predicts all semantic role labels for all given word pairs in the sentence without relying on any syntactic parse information. Specifically, we augment the BiLSTM encoder with a non-linear transformation to further distinguish the predicate and the argument in a given sentence, and model the semantic role labeling process as a word pair classification task by employing the biaffine attentional mechanism. Though the proposed model is syntax-agnostic with local decoder, it outperforms the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009 benchmarks for both English and Chinese. To our best knowledge, we report the first syntax-agnostic SRL model that surpasses all known syntax-aware models.

Connected through hyperedge HE-1 (induced by source paper "capturing argument interaction in semantic role labeling with capsule networks"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "dependency or span end to end uniform semantic role labeling"
    - "polyglot semantic role labeling"
    - "multi labeled relation extraction with attentive capsule network"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-2 (induced by source paper "syntax aware neural semantic role labeling"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "tag enhanced tree structured neural networks for implicit discourse relation classification"
    - "deep contextualized word representations"
    - "linguistically informed self attention for semantic role labeling"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-3 (induced by source paper "syntax enhanced self attention based semantic role labeling"):
  with members:
    - "syntax aware neural semantic role labeling"
    - "self attention with relative position representations"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "span model for open information extraction on accurate corpus"):
  with members:
    - "global greedy dependency parsing"
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "graphene a context preserving open information extraction system"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "neural open information extraction"
    - "end to end neural coreference resolution"

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)