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: "document context language models"
  Abstract: Text documents are structured on multiple levels of detail: individual words are related by syntax, but larger units of text are related by discourse structure. Existing language models generally fail to account for discourse structure, but it is crucial if we are to have language models that reward coherence and generate coherent texts. We present and empirically evaluate a set of multi-level recurrent neural network language models, called Document-Context Language Models (DCLM), which incorporate contextual information both within and beyond the sentence. In comparison with word-level recurrent neural network language models, the DCLM models obtain slightly better predictive likelihoods, and considerably better assessments of document coherence.

Connected through hyperedge HE-1 (induced by source paper "transformer xl attentive language models beyond a fixed length context"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-2 (induced by source paper "acoustic to word models with conversational context information"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "dialog context aware end to end speech recognition"
    - "multi modal data augmentation for end to end asr"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "english conversational telephone speech recognition by humans and machines"

Connected through hyperedge HE-3 (induced by source paper "gated embeddings in end to end speech recognition for conversational context fusion"):
  with members:
    - "advancing acoustic to word ctc model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved training of end to end attention models for speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "towards language universal end to end speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-4 (induced by source paper "calibration entropy rates and memory in language models"):
  with members:
    - "direct output connection for a high rank language model"
    - "frage frequency agnostic word representation"
    - "an analysis of neural language modeling at multiple scales"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "information theoretic co training"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "on calibration of modern neural networks"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-5 (induced by source paper "dialog context aware end to end speech recognition"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "comparison of decoding strategies for ctc acoustic models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "dialog context language modeling with recurrent neural networks"
    - "very deep convolutional networks for end to end speech recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-6 (induced by source paper "context models for oov word translation in low resource languages"):
  with members:
    - "attention is all you need"
    - "improving lexical choice in neural machine translation"
    - "six challenges for neural machine translation"
    - "pointing the unknown words"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "pointer sentinel mixture models"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "contextual neural model for translating bilingual multi speaker conversations"):
  with members:
    - "context aware neural machine translation learns anaphora resolution"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "learning to remember translation history with a continuous cache"
    - "evaluating discourse phenomena in neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "massive exploration of neural machine translation architectures"
    - "document context neural machine translation with memory networks"
    - "incorporating structural alignment biases into an attentional neural translation model"

Connected through hyperedge HE-8 (induced by source paper "learning to remember translation history with a continuous cache"):
  with members:
    - "translating pro drop languages with reconstruction models"
    - "translating phrases in neural machine translation"
    - "frustratingly short attention spans in neural language modeling"
    - "chunk based bi scale decoder for neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "learning to remember rare events"
    - "modeling source syntax for neural machine translation"
    - "learning to create and reuse words in open vocabulary neural language modeling"

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)