human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  Abstract: Named entity recognition (NER) models are typically based on the architecture of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the modeling of single input prevent the full utilization of global information from larger scope, not only in the entire sentence, but also in the entire document (dataset). In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation. In sentence-level, we take different contributions of words in a single sentence into consideration to enhance the sentence representation learned from an independent BiLSTM via label embedding attention mechanism. In document-level, the key-value memory network is adopted to record the document-aware information for each unique word which is sensitive to similarity of context information. Our two-level hierarchical contextualized representations are fused with each input token embedding and corresponding hidden state of BiLSTM, respectively. The experimental results on three benchmark NER datasets (CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset) show that we establish new state-of-the-art results.

Includes members:
  Member "semantics aware bert for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "grn gated relation network to enhance convolutional neural network for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can ner convolutional attention network for chinese named entity recognition")
  Member "syntax aware multilingual semantic role labeling":
    also connected through other hyperedges:
      (none)
  Member "lattice based transformer encoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "gcdt a global context enhanced deep transition architecture for sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving slot filling by utilizing contextual information")
  Member "dual co matching network for multi choice reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the comparability of pre trained language models")
  Member "robust lexical features for improved neural network named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention based conditioning methods for external knowledge integration")
  Member "contextualized non local neural networks for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")

Question: Please tell me which class the source paper 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)