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 decoding as continuous optimization in neural machine translation"
  Abstract: We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We convert decoding - basically a discrete optimization problem - into a continuous optimization problem. The resulting constrained continuous optimisation problem is then tackled using gradient-based methods. Our powerful decoding framework enables decoding intractable models such as the intersection of left-to-right and right-to-left (bidirectional) as well as source-to-target and target-to-source (bilingual) NMT models. Our empirical results show that our decoding framework is effective, and leads to substantial improvements in translations generated from the intersected models where the typical greedy or beam search is not feasible. We also compare our framework against reranking, and analyse its advantages and disadvantages.

Connected through hyperedge HE-1 (induced by source paper "improving bidirectional decoding with dynamic target semantics in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "synchronous bidirectional inference for neural sequence generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "neural machine translation with key value memory augmented attention"

Connected through hyperedge HE-2 (induced by source paper "deterministic non autoregressive neural sequence modeling by iterative refinement"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "unsupervised machine translation using monolingual corpora only"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "state of the art speech recognition with sequence to sequence models"
    - "non autoregressive neural machine translation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "asynchronous bidirectional decoding for neural machine translation"):
  with members:
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "neural system combination for machine translation"
    - "deep neural machine translation with linear associative unit"
    - "twin networks matching the future for sequence generation"
    - "memory enhanced decoder for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with recurrent attention modeling"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-4 (induced by source paper "differentiable scheduled sampling for credit assignment"):
  with members:
    - "sequence to sequence learning as beam search optimization"
    - "globally normalized transition based neural networks"
    - "an actor critic algorithm for sequence prediction"
    - "a neural attention model for abstractive sentence summarization"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning to transduce with unbounded memory"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "charmanteau character embedding models for portmanteau creation"):
  with members:
    - "generating appealing brand names"
    - "the neural noisy channel"
    - "multi source neural translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)