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 "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"):
  Abstract: While neural machine translation (NMT) models provide improved translation quality in an elegant, end-to-end framework, it is less clear what they learn about language. Recent work has started evaluating the quality of vector representations learned by NMT models on morphological and syntactic tasks. In this paper, we investigate the representations learned at different layers of NMT encoders. We train NMT systems on parallel data and use the trained models to extract features for training a classifier on two tasks: part-of-speech and semantic tagging. We then measure the performance of the classifier as a proxy to the quality of the original NMT model for the given task. Our quantitative analysis yields interesting insights regarding representation learning in NMT models. For instance, we find that higher layers are better at learning semantics while lower layers tend to be better for part-of-speech tagging. We also observe little effect of the target language on source-side representations, especially with higher quality NMT models.

Includes members:
  Member "analyzing hidden representations in end to end automatic speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "untangling in invariant speech recognition")
  Member "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "untangling in invariant speech recognition")
  Member "encoding of phonology in a recurrent neural model of grounded speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "semantic tagging with deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parsing thai social data a new challenge for thai nlp")
  Member "fine grained analysis of sentence embeddings using auxiliary prediction tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "representation of linguistic form and function in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on architectures for including visual information in neural language models for image description")
  Member "word representation models for morphologically rich languages in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the linguistic representational power of neural machine translation models")

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)