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 "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.

Includes members:
  Member "exploiting sentential context for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self attention with structural position representations")
  Member "transformers with convolutional context for asr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "self attention with structural position representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self attentive hawkes processes")
  Member "modeling recurrence for transformer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "adaptive attention span in transformers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta embeddings based on self attention")
  Member "exploiting deep representations for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gret global representation enhanced transformer")
  Member "modeling localness for self attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gret global representation enhanced transformer")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)