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: "two discourse driven language models for semantics"
  Abstract: Natural language understanding often requires deep semantic knowledge. Expanding on previous proposals, we suggest that some important aspects of semantic knowledge can be modeled as a language model if done at an appropriate level of abstraction. We develop two distinct models that capture semantic frame chains and discourse information while abstracting over the specific mentions of predicates and entities. For each model, we investigate four implementations: a "standard" N-gram language model and three discriminatively trained "neural" language models that generate embeddings for semantic frames. The quality of the semantic language models (SemLM) is evaluated both intrinsically, using perplexity and a narrative cloze test and extrinsically - we show that our SemLM helps improve performance on semantic natural language processing tasks such as co-reference resolution and discourse parsing.

Connected through hyperedge HE-1 (induced by source paper "incorporating structured commonsense knowledge in story completion"):
  with members:
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "know what you don t know unanswerable questions for squad"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "dynamic fusion networks for machine reading comprehension"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "augmenting end to end dialog systems with commonsense knowledge"

Connected through hyperedge HE-2 (induced by source paper "hierarchical quantized representations for script generation"):
  with members:
    - "toward controlled generation of text"
    - "learning hierarchical features from generative models"
    - "event representations with tensor based compositions"
    - "neural discrete representation learning"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "narrative modeling with memory chains and semantic supervision"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "story cloze ending selection baselines and data examination"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "improving temporal relation extraction with a globally acquired statistical resource"):
  with members:
    - "a structured learning approach to temporal relation extraction"

Connected through hyperedge HE-5 (induced by source paper "a neural language model for dynamically representing the meanings of unknown words and entities in a discourse"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "dynamic entity representations in neural language models"
    - "pointing the unknown words"
    - "improving coreference resolution by learning entity level distributed representations"
    - "context dependent word representation for neural machine translation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "language modeling with gated convolutional networks"

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)