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: "context dependent word representation for neural machine translation"
  Abstract: We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedding vector, of a symbol encodes multiple dimensions of similarity, equivalent to encoding more than one meaning of the word. This has the consequence that the encoder and decoder recurrent networks in neural machine translation need to spend substantial amount of their capacity in disambiguating source and target words based on the context which is defined by a source sentence. Based on this observation, in this paper we propose to contextualize the word embedding vectors using a nonlinear bag-of-words representation of the source sentence. Additionally, we propose to represent special tokens (such as numbers, proper nouns and acronyms) with typed symbols to facilitate translating those words that are not well-suited to be translated via continuous vectors. The experiments on En-Fr and En-De reveal that the proposed approaches of contextualization and symbolization improves the translation quality of neural machine translation systems significantly.

Connected through hyperedge HE-1 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-2 (induced by source paper "can unconditional language models recover arbitrary sentences"):
  with members:
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "measuring the intrinsic dimension of objective landscapes"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "context aware self attention networks"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "deep contextualized pairwise semantic similarity for arabic language questions"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning semantic textual similarity from conversations"
    - "a deep network model for paraphrase detection in short text messages"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "self knowledge distillation in natural language processing"):
  with members:
    - "attention is all you need"
    - "a neural knowledge language model"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "yolo9000 better faster stronger"
    - "pointer sentinel mixture models"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "network intrusion detection based on lstm and feature embedding"):
  with members:
    - "performance comparison of intrusion detection systems and application of machine learning to snort system"
    - "persistent hidden states and nonlinear transformation for long short term memory"
    - "lstm a search space odyssey"
    - "entity embeddings of categorical variables"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "time masking leveraging temporal information in spoken dialogue systems"):
  with members:
    - "contextual slot carryover for disparate schemas"
    - "scalable multi domain dialogue state tracking"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "neural belief tracker data driven dialogue state tracking"

Connected through hyperedge HE-8 (induced by source paper "fine grained attention mechanism for neural machine translation"):
  with members:
    - "character based neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "modeling coverage for neural machine translation"
    - "edinburgh neural machine translation systems for wmt 16"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "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)