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: "learning to segment inputs for nmt favors character level processing"
  Abstract: Most modern neural machine translation (NMT) systems rely on presegmented inputs. Segmentation granularity importantly determines the input and output sequence lengths, hence the modeling depth, and source and target vocabularies, which in turn determine model size, computational costs of softmax normalization, and handling of out-of-vocabulary words. However, the current practice is to use static, heuristic-based segmentations that are fixed before NMT training. This begs the question whether the chosen segmentation is optimal for the translation task. To overcome suboptimal segmentation choices, we present an algorithm for dynamic segmentation based on the Adaptative Computation Time algorithm (Graves 2016), that is trainable end-to-end and driven by the NMT objective. In an evaluation on four translation tasks we found that, given the freedom to navigate between different segmentation levels, the model prefers to operate on (almost) character level, providing support for purely character-level NMT models from a novel angle.

Connected through hyperedge HE-1 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "learning when to concentrate or divert attention self adaptive attention temperature for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "controlling the output length of neural machine translation"):
  with members:
    - "positional encoding to control output sequence length"
    - "revisiting character based neural machine translation with capacity and compression"
    - "achieving human parity on automatic chinese to english news translation"
    - "multi task neural models for translating between styles within and across languages"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "scaling neural machine translation"
    - "neural machine translation into language varieties"

Connected through hyperedge HE-4 (induced by source paper "adapting multilingual neural machine translation to unseen languages"):
  with members:
    - "generalized data augmentation for low resource translation"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "revisiting character based neural machine translation with capacity and compression"
    - "universal neural machine translation for extremely low resource languages"
    - "scaling neural machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "rapid adaptation of neural machine translation to new languages"

Connected through hyperedge HE-5 (induced by source paper "a latent morphology model for open vocabulary neural machine translation"):
  with members:
    - "on the importance of word boundaries in character level neural machine translation"
    - "interpretable neural predictions with differentiable binary variables"
    - "revisiting character based neural machine translation with capacity and compression"
    - "are all languages equally hard to language model"
    - "character level models versus morphology in semantic role labeling"
    - "from characters to words to in between do we capture morphology"
    - "opennmt open source toolkit for neural machine translation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"

Connected through hyperedge HE-6 (induced by source paper "multitask learning for different subword segmentations in neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention is all you need"
    - "exploiting linguistic resources for neural machine translation using multi task learning"

Connected through hyperedge HE-7 (induced by source paper "bpe dropout simple and effective subword regularization"):
  with members:
    - "a call for prudent choice of subword merge operations in neural machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "finding better subword segmentation for neural machine translation"
    - "unsupervised statistical machine translation"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "frage frequency agnostic word representation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-8 (induced by source paper "on the importance of word boundaries in character level neural machine translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "deep architectures for neural machine translation"
    - "linguistically motivated vocabulary reduction for neural machine translation from turkish to english"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "character based neural machine translation"

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)