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: "morph fitting fine tuning word vector spaces with simple language specific rules"
  Abstract: Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that 'inexpensive' is a rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.

Connected through hyperedge HE-1 (induced by source paper "attentive mimicking better word embeddings by attending to informative contexts"):
  with members:
    - "incorporating subword information into matrix factorization word embeddings"
    - "evaluating word embeddings in multi label classification using fine grained name typing"
    - "learning semantic representations for novel words leveraging both form and context"
    - "attention is all you need"
    - "mimicking word embeddings using subword rnns"
    - "high risk learning acquiring new word vectors from tiny data"
    - "enriching word vectors with subword information"
    - "ultradense word embeddings by orthogonal transformation"

Connected through hyperedge HE-2 (induced by source paper "how does grammatical gender affect noun representations in gender marking languages"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "demorphy german language morphological analyzer"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "learning gender neutral word embeddings"
    - "semantics derived automatically from language corpora contain human like biases"
    - "the interplay of semantics and morphology in word embeddings"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-3 (induced by source paper "a simple and effective approach for fine tuning pre trained word embeddings for improved text classification"):
  with members:
    - "niletmrg at semeval 2017 task 4 arabic sentiment analysis"
    - "supervised fine tuning for word embedding with integrated knowledge"
    - "distributed representations of sentences and documents"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-5 (induced by source paper "hyperlex a large scale evaluation of graded lexical entailment"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "baselines and test data for cross lingual inference"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a decomposable attention model for natural language inference"
    - "intrinsic subspace evaluation of word embedding representations"
    - "counter fitting word vectors to linguistic constraints"
    - "problems with evaluation of word embeddings using word similarity tasks"

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)