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 span selection model for semantic role labeling"
  Abstract: We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.

Connected through hyperedge HE-1 (induced by source paper "span based joint entity and relation extraction with transformer pre training"):
  with members:
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "spanbert improving pre training by representing and predicting spans"
    - "scibert a pretrained language model for scientific text"
    - "neural metric learning for fast end to end relation extraction"
    - "entity relation extraction as multi turn question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "end to end neural relation extraction using deep biaffine attention"

Connected through hyperedge HE-2 (induced by source paper "translate and label an encoder decoder approach for cross lingual semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "polyglot semantic role labeling"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "allennlp a deep semantic natural language processing platform"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "a syntax aware multi task learning framework for chinese semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "syntax aware neural semantic role labeling"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "graph convolutions over constituent trees for syntax aware semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "syntactic scaffolds for semantic structures"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "linguistically informed self attention for semantic role labeling"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "parsing all syntax and semantics dependencies and spans"):
  with members:
    - "syntax aware neural semantic role labeling with supertags"
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "multilingual constituency parsing with self attention and pre training"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (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"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "constituent parsing as sequence labeling"
    - "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-7 (induced by source paper "multi task learning for japanese predicate argument structure analysis"):
  with members:
    - "distance free modeling of multi predicate interactions in end to end japanese predicate argument structure analysis"
    - "linguistically informed self attention for semantic role labeling"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "deep semantic role labeling with self attention"
    - "srl4orl improving opinion role labelling using multi task learning with semantic role labeling"
    - "adversarial multi task learning for text classification"
    - "revisiting the design issues of local models for japanese predicate argument structure analysis"
    - "a joint many task model growing a neural network for multiple nlp tasks"

Connected through hyperedge HE-8 (induced by source paper "simple bert models for relation extraction and semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "simplifying graph convolutional networks"
    - "improving relation extraction by pre trained language representations"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "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"

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)