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: "towards better modeling hierarchical structure for self attention with ordered neurons"
  Abstract: Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work. With the belief that modeling hierarchical structure is an essential complementary between SANs and RNNs, we propose to further enhance the strength of hybrid models with an advanced variant of RNNs - Ordered Neurons LSTM (ON-LSTM), which introduces a syntax-oriented inductive bias to perform tree-like composition. Experimental results on the benchmark machine translation task show that the proposed approach outperforms both individual architectures and a standard hybrid model. Further analyses on targeted linguistic evaluation and logical inference tasks demonstrate that the proposed approach indeed benefits from a better modeling of hierarchical structure.

Connected through hyperedge HE-1 (induced by source paper "automatic business process structure discovery using ordered neurons lstm a preliminary study"):
  with members:
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "attention is all you need"
    - "effective inference for generative neural parsing"

Connected through hyperedge HE-2 (induced by source paper "self attention with structural position representations"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting sentential context for neural machine translation"
    - "modeling recurrence for transformer"
    - "semantic neural machine translation using amr"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-3 (induced by source paper "multi granularity self attention for neural machine translation"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "area attention"
    - "information aggregation for multi head attention with routing by agreement"
    - "strategies for structuring story generation"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "modeling recurrence for transformer"
    - "phrase based attentions"
    - "bi directional block self attention for fast and memory efficient sequence modeling"

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)