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: "integrative semantic dependency parsing via efficient large scale feature selection"
  Abstract: Semantic parsing, i.e., the automatic derivation of meaning representation such as an instantiated predicate-argument structure for a sentence, plays a critical role in deep processing of natural language. Unlike all other top systems of semantic dependency parsing that have to rely on a pipeline framework to chain up a series of submodels each specialized for a specific subtask, the one presented in this article integrates everything into one model, in hopes of achieving desirable integrity and practicality for real applications while maintaining a competitive performance. This integrative approach tackles semantic parsing as a word pair classification problem using a maximum entropy classifier. We leverage adaptive pruning of argument candidates and large-scale feature selection engineering to allow the largest feature space ever in use so far in this field, it achieves a state-of-the-art performance on the evaluation data set for CoNLL-2008 shared task, on top of all but one top pipeline system, confirming its feasibility and effectiveness.

Connected through hyperedge HE-1 (induced by source paper "semantics aware bert for language understanding"):
  with members:
    - "limit bert linguistic informed multi task bert"
    - "dependency or span end to end uniform semantic role labeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "sg net syntax guided machine reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "semantic role labeling with associated memory network"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "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"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-3 (induced by source paper "syntax aware multilingual semantic role labeling"):
  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"
    - "polyglot semantic role labeling"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "dependency or span end to end uniform semantic role labeling"):
  with members:
    - "i know what you want semantic learning for text comprehension"
    - "learning joint semantic parsers from disjoint data"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "deep semantic role labeling with self attention"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-5 (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 full end to end semantic role labeler syntax agnostic over syntax aware"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "neural open information extraction"

Connected through hyperedge HE-6 (induced by source paper "effective representation for easy first dependency parsing"):
  with members:
    - "exploring recombination for efficient decoding of neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "syntax directed attention for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-7 (induced by source paper "attentive semantic role labeling with boundary indicator"):
  with members:
    - "question answering as global reasoning over semantic abstractions"
    - "effective character augmented word embedding for machine reading comprehension"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-8 (induced by source paper "a full end to end semantic role labeler syntax agnostic over syntax aware"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "fast and accurate neural word segmentation for chinese"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "neural semantic role labeling with dependency path embeddings"
    - "effective approaches to attention based neural machine translation"

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)