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: "lattice based transformer encoder for neural machine translation"
  Abstract: Neural machine translation (NMT) takes deterministic sequences for source representations. However, either word-level or subword-level segmentations have multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. We hypothesize that the diversity in segmentations may affect the NMT performance. To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training. We propose two methods: 1) lattice positional encoding and 2) lattice-aware self-attention. These two methods can be used together and show complementary to each other to further improve translation performance. Experiment results show superiorities of lattice-based encoders in word-level and subword-level representations over conventional Transformer encoder.

Connected through hyperedge HE-1 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "robust lexical features for improved neural network named entity recognition"
    - "contextualized non local neural networks for sequence learning"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"

Connected through hyperedge HE-3 (induced by source paper "dual co matching network for multi choice reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "hierarchical contextualized representation for named entity recognition"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "sg net syntax guided machine reading comprehension"
    - "evidence sentence extraction for machine reading comprehension"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "story ending prediction by transferable bert"

Connected through hyperedge HE-4 (induced by source paper "porous lattice based transformer encoder for chinese ner"):
  with members:
    - "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"
    - "lattice transformer for speech translation"
    - "self attentional models for lattice inputs"
    - "neural collective entity linking based on recurrent random walk network learning"
    - "chinese ner using lattice lstm"
    - "modeling localness for self attention networks"
    - "convolutional self attention network"
    - "combining discrete and neural features for sequence labeling"

Connected through hyperedge HE-5 (induced by source paper "korean to chinese machine translation using chinese character as pivot clue"):
  with members:
    - "semantic role labeling with associated memory network"
    - "dependency or span end to end uniform semantic role labeling"
    - "opennmt neural machine translation toolkit"
    - "finding better subword segmentation for neural machine translation"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "attention is all you need"
    - "attention focusing for neural machine translation by bridging source and target embeddings"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-6 (induced by source paper "open vocabulary learning for neural chinese pinyin ime"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "character word lstm language models"

Connected through hyperedge HE-7 (induced by source paper "neural based pinyin to character conversion with adaptive vocabulary"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "vocabulary selection strategies for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"

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)