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: "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
  Abstract: Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications. In this paper, we perform an extrinsic evaluation of five popular word embedding methods in the context of four sequence labelling tasks: POS-tagging, syntactic chunking, NER and MWE identification. A particular focus of the paper is analysing the effects of task-based updating of word representations. We show that when using word embeddings as features, as few as several hundred training instances are sufficient to achieve competitive results, and that word embeddings lead to improvements over OOV words and out of domain. Perhaps more surprisingly, our results indicate there is little difference between the different word embedding methods, and that simple Brown clusters are often competitive with word embeddings across all tasks we consider.

Connected through hyperedge HE-1 (induced by source paper "shoma at parseme shared task on automatic identification of vmwes neural multiword expression tagging with high generalisation"):
  with members:
    - "fine tuned language models for text classification"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"):
  with members:
    - "expanding subjective lexicons for social media mining with embedding subspaces"
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "creating causal embeddings for question answering with minimal supervision"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "non distributional word vector representations"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "a primer on neural network models for natural language processing"
    - "wordrep a benchmark for research on learning word representations"

Connected through hyperedge HE-3 (induced by source paper "sparse coding of neural word embeddings for multilingual sequence labeling"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "compressing neural language models by sparse word representations"
    - "unsupervised pos induction with word embeddings"
    - "sparse overcomplete word vector representations"
    - "learning word representations with hierarchical sparse coding"
    - "polyglot distributed word representations for multilingual nlp"
    - "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 "from incremental meaning to semantic unit phrase by phrase"):
  with members:
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "named entity recognition for novel types by transfer learning"):
  with members:
    - "how transferable are features in deep neural networks"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "mapping unseen words to task trained embedding spaces"):
  with members:
    - "finding function in form compositional character models for open vocabulary word representation"
    - "skip thought vectors"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "compositional morphology for word representations and language modelling"
    - "convolutional neural networks for sentence classification"
    - "on using very large target vocabulary for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "word representations tree models and syntactic functions"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "factorial hidden markov models for learning representations of natural language"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "syntactic topic models"

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)