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: "double path networks for sequence to sequence learning"
  Abstract: Encoder-decoder based Sequence to Sequence learning (S2S) has made remarkable progress in recent years. Different network architectures have been used in the encoder/decoder. Among them, Convolutional Neural Networks (CNN) and Self Attention Networks (SAN) are the prominent ones. The two architectures achieve similar performances but use very different ways to encode and decode context: CNN use convolutional layers to focus on the local connectivity of the sequence, while SAN uses self-attention layers to focus on global semantics. In this work we propose Double Path Networks for Sequence to Sequence learning (DPN-S2S), which leverage the advantages of both models by using double path information fusion. During the encoding step, we develop a double path architecture to maintain the information coming from different paths with convolutional layers and self-attention layers separately. To effectively use the encoded context, we develop a cross attention module with gating and use it to automatically pick up the information needed during the decoding step. By deeply integrating the two paths with cross attention, both types of information are combined and well exploited. Experiments show that our proposed method can significantly improve the performance of sequence to sequence learning over state-of-the-art systems.

Connected through hyperedge HE-1 (induced by source paper "fairseq a fast extensible toolkit for sequence modeling"):
  with members:
    - "strategies for structuring story generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "self attention with relative position representations"
    - "bottom up abstractive summarization"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

Connected through hyperedge HE-2 (induced by source paper "mass masked sequence to sequence pre training for language generation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised statistical machine translation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-3 (induced by source paper "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-4 (induced by source paper "sentence wise smooth regularization for sequence to sequence learning"):
  with members:
    - "frage frequency agnostic word representation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "dense information flow for neural machine translation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "sequence to sequence learning as beam search optimization"
    - "cutting off redundant repeating generations for neural abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "non autoregressive neural machine translation with enhanced decoder input"):
  with members:
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "analyzing uncertainty in neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "fast decoding in sequence models using discrete latent variables"
    - "dense information flow for neural machine translation"
    - "attention is all you need"
    - "non autoregressive neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "beyond error propagation in neural machine translation characteristics of language also matter"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "a study of reinforcement learning for neural machine translation"
    - "dense information flow for neural machine translation"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "edinburgh neural machine translation systems for wmt 16"
    - "minimum risk training for neural machine translation"
    - "neural machine translation of rare words with subword units"

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)