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 "structured attention networks"):
  Abstract: Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer structural dependencies without abandoning end-to-end training. In this work, we experiment with incorporating richer structural distributions, encoded using graphical models, within deep networks. We show that these structured attention networks are simple extensions of the basic attention procedure, and that they allow for extending attention beyond the standard soft-selection approach, such as attending to partial segmentations or to subtrees. We experiment with two different classes of structured attention networks: a linear-chain conditional random field and a graph-based parsing model, and describe how these models can be practically implemented as neural network layers. Experiments show that this approach is effective for incorporating structural biases, and structured attention networks outperform baseline attention models on a variety of synthetic and real tasks: tree transduction, neural machine translation, question answering, and natural language inference. We further find that models trained in this way learn interesting unsupervised hidden representations that generalize simple attention.

Includes members:
  Member "textual entailment with structured attentions and composition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "online segment to segment neural transduction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs")
  Member "enhanced lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial filters of dataset biases")
  Member "segmental recurrent neural networks for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graphs")
  Member "neural tree indexers for text understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ordered memory")
  Member "the neural noisy channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training")
  Member "end to end memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)