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 "improving aspect term extraction with bidirectional dependency tree representation"):
  Abstract: Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that dependency tree structure representation is promising for this task. In this paper, we propose a novel bidirectional dependency tree network to extract dependency structure features from the given sentences. The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree. An end-to-end framework is proposed to integrate the embedded representations and BiLSTM plus CRF to learn both tree-structured and sequential features to solve the aspect term extraction problem. Experimental results demonstrate that the proposed model outperforms state-of-the-art baseline models on four benchmark SemEval datasets.

Includes members:
  Member "lifelong learning crf for supervised aspect extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis")
  Member "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hamner headword amplified multi span distantly supervised method for domain specific named entity recognition")
  Member "improved neural machine translation with a syntax aware encoder and decoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "multi task memory networks for category specific aspect and opinion terms co extraction":
    also connected through other hyperedges:
      (none)
  Member "unsupervised word and dependency path embeddings for aspect term extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comprehensive analysis of aspect term extraction methods using various text embeddings")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "bidirectional tree structured lstm with head lexicalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations")
  Member "end to end relation extraction using lstms on sequences and tree structures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deeper task specificity improves joint entity and relation extraction")

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)