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: "modeling order in neural word embeddings at scale"
  Abstract: Natural Language Processing (NLP) systems commonly leverage bag-of-words co-occurrence techniques to capture semantic and syntactic word relationships. The resulting word-level distributed representations often ignore morphological information, though character-level embeddings have proven valuable to NLP tasks. We propose a new neural language model incorporating both word order and character order in its embedding. The model produces several vector spaces with meaningful substructure, as evidenced by its performance of 85.8% on a recent word-analogy task, exceeding best published syntactic word-analogy scores by a 58% error margin. Furthermore, the model includes several parallel training methods, most notably allowing a skip-gram network with 160 billion parameters to be trained overnight on 3 multi-core CPUs, 14x larger than the previous largest neural network.

Connected through hyperedge HE-1 (induced by source paper "utilizing character and word embeddings for text normalization with sequence to sequence models"):
  with members:
    - "attention based encoder decoder networks for spelling and grammatical error correction"
    - "deep contextualized word representations"
    - "attention is all you need"
    - "neural language correction with character based attention"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "syntree2vec an algorithm to augment syntactic hierarchy into word embeddings"):
  with members:
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "syntaxnet models for the conll 2017 shared task"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "byzantine tolerant machine learning"):
  with members:
    - "training very deep networks"
    - "deep learning with elastic averaging sgd"

Connected through hyperedge HE-4 (induced by source paper "a growing long term episodic semantic memory"):
  with members:
    - "progressive neural networks"
    - "learning feed forward one shot learners"
    - "learning without forgetting"
    - "adanet adaptive structural learning of artificial neural networks"
    - "hierarchical multiscale recurrent neural networks"
    - "control of memory active perception and action in minecraft"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"

Connected through hyperedge HE-5 (induced by source paper "bilingual distributed word representations from document aligned comparable data"):
  with members:
    - "skip thought vectors"
    - "learning to understand phrases by embedding the dictionary"
    - "distributed representations of sentences and documents"
    - "leveraging monolingual data for crosslingual compositional word representations"
    - "improving zero shot learning by mitigating the hubness problem"
    - "multilingual models for compositional distributed semantics"
    - "evaluating neural word representations in tensor based compositional settings"
    - "an autoencoder approach to learning bilingual word representations"

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)