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: "neural lattice to sequence models for uncertain inputs"
  Abstract: The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM's child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.

Connected through hyperedge HE-1 (induced by source paper "lattice based transformer encoder for neural machine translation"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "chinese word segmentation another decade review 2007 2017"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "revisiting character based neural machine translation with capacity and compression"
    - "nict s neural and statistical machine translation systems for the wmt18 news translation task"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "finding better subword segmentation for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "self attentional models for lattice inputs"):
  with members:
    - "hybrid self attention network for machine translation"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for named entity recognition"
    - "attention is all you need"
    - "on the fly operation batching in dynamic computation graphs"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"
    - "lattice based recurrent neural network encoders for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "lattice transformer for speech translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised multi modal neural machine translation"
    - "chinese ner using lattice lstm"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "bilingual expert can find translation errors"
    - "self attention with relative position representations"
    - "neural lattice language models"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-5 (induced by source paper "breaking the data barrier towards robust speech translation via adversarial stability training"):
  with members:
    - "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"
    - "towards robust neural machine translation"
    - "towards end to end spoken language understanding"
    - "end to end automatic speech translation of audiobooks"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "improving the robustness of speech translation"
    - "sequence to sequence models can directly translate foreign speech"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "mitigating noisy inputs for question answering"):
  with members:
    - "studio ousia s quiz bowl question answering system"
    - "odsqa open domain spoken question answering dataset"
    - "mtnt a testbed for machine translation of noisy text"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "synthetic and natural noise both break neural machine translation"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-7 (induced by source paper "chinese ner using lattice lstm"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "combining discrete and neural features for sequence labeling"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "lattice long short term memory for human action recognition"
    - "yedda a lightweight collaborative text span annotation tool"

Connected through hyperedge HE-8 (induced by source paper "subword regularization improving neural network translation models with multiple subword candidates"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "data noising as smoothing in neural network language models"
    - "synthetic and natural noise both break neural machine translation"
    - "convolutional sequence to sequence learning"
    - "lattice based recurrent neural network encoders for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and 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)