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: "revisiting the design issues of local models for japanese predicate argument structure analysis"
  Abstract: The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions. However, the existing global models tend to employ only relatively simple local features; therefore, the overall performance gains are rather limited. The importance of designing a local model is demonstrated in this study by showing that the performance of a sophisticated local model can be considerably improved with recent feature embedding methods and a feature combination learning based on a neural network, outperforming the state-of-the-art global models in $F_1$ on a common benchmark dataset.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for japanese predicate argument structure analysis"):
  with members:
    - "a span selection model for semantic role labeling"
    - "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"
    - "a joint many task model growing a neural network for multiple nlp tasks"

Connected through hyperedge HE-2 (induced by source paper "distance free modeling of multi predicate interactions in end to end japanese predicate argument structure analysis"):
  with members:
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "a structured self attentive sentence embedding"
    - "a deep reinforced model for abstractive summarization"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "improving coreference resolution by learning entity level distributed representations"
    - "deep residual learning for image recognition"

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)