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: "character word lstm language models"
  Abstract: We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77% relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57% on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters.

Connected through hyperedge HE-1 (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"
    - "learning to segment inputs for nmt favors character level processing"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "a survey on neural network language models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "theanolm an extensible toolkit for neural network language modeling"
    - "gated word character recurrent language model"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "coherent dialogue with attention based language models"

Connected through hyperedge HE-4 (induced by source paper "stem driven language models for morphologically rich languages"):
  with members:
    - "morphological segmentation inside out"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "a joint model for word embedding and word morphology"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "generalizing and hybridizing count based and neural language models"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "subword augmented embedding for cloze reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "fast and accurate neural word segmentation for chinese"
    - "the interplay of semantics and morphology in word embeddings"

Connected through hyperedge HE-7 (induced by source paper "open vocabulary learning for neural chinese pinyin ime"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "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"

Connected through hyperedge HE-8 (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"
    - "lattice based transformer encoder for neural machine translation"
    - "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"
    - "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)