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: "domain and function a dual space model of semantic relations and compositions"
  Abstract: Given appropriate representations of the semantic relations between carpenter and wood and between mason and stone (for example, vectors in a vector space model), a suitable algorithm should be able to recognize that these relations are highly similar (carpenter is to wood as mason is to stone; the relations are analogous). Likewise, with representations of dog, house, and kennel, an algorithm should be able to recognize that the semantic composition of dog and house, dog house, is highly similar to kennel (dog house and kennel are synonymous). It seems that these two tasks, recognizing relations and compositions, are closely connected. However, up to now, the best models for relations are significantly different from the best models for compositions. In this paper, we introduce a dual-space model that unifies these two tasks. This model matches the performance of the best previous models for relations and compositions. The dual-space model consists of a space for measuring domain similarity and a space for measuring function similarity. Carpenter and wood share the same domain, the domain of carpentry. Mason and stone share the same domain, the domain of masonry. Carpenter and mason share the same function, the function of artisans. Wood and stone share the same function, the function of materials. In the composition dog house, kennel has some domain overlap with both dog and house (the domains of pets and buildings). The function of kennel is similar to the function of house (the function of shelters). By combining domain and function similarities in various ways, we can model relations, compositions, and other aspects of semantics.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "fair is better than sensational man is to doctor as woman is to doctor"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "domain representation for knowledge graph embedding"):
  with members:
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "locally adaptive translation for knowledge graph embedding"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-4 (induced by source paper "on the dimensionality of word embedding"):
  with members:
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "enriching word vectors with subword information"
    - "spectrum estimation from samples"
    - "neural architectures for named entity recognition"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "unsupervised post processing of word vectors via conceptor negation"):
  with members:
    - "fully statistical neural belief tracking"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "all but the top simple and effective postprocessing for word representations"
    - "a network based end to end trainable task oriented dialogue system"
    - "enriching word vectors with subword information"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-6 (induced by source paper "uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation"):
  with members:
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a decomposable attention model for natural language inference"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "nonparametric spherical topic modeling with word embeddings"
    - "ultradense word embeddings by orthogonal transformation"
    - "a latent variable model approach to pmi based word embeddings"
    - "sparse overcomplete word vector representations"

Connected through hyperedge HE-7 (induced by source paper "term set expansion based on multi context term embeddings an end to end workflow"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "semi supervised emotion lexicon expansion with label propagation and specialized word embeddings"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "crowdsourcing a word emotion association lexicon"
    - "natural language processing almost from scratch"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

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)