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: "swivel improving embeddings by noticing what s missing"
  Abstract: We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods.

Connected through hyperedge HE-1 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-2 (induced by source paper "vector representations of text data in deep learning"):
  with members:
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "starspace embed all the things"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "node2vec scalable feature learning for networks"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"

Connected through hyperedge HE-3 (induced by source paper "deconstructing and reconstructing word embedding algorithms"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi task learning for sequence tagging an empirical study"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reconstruction of word embeddings from sub word parameters"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-4 (induced by source paper "look read and enrich learning from scientific figures and their captions"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "deep contextualized word representations"
    - "construction of the literature graph in semantic scholar"
    - "learning two branch neural networks for image text matching tasks"
    - "look listen and learn"
    - "vse improving visual semantic embeddings with hard negatives"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "bridging the gap for tokenizer free language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"
    - "universal language model fine tuning for text classification"
    - "character level language modeling with deeper self attention"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-6 (induced by source paper "single training dimension selection for word embedding with pca"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "nationality classification using name embeddings"
    - "simple and effective dimensionality reduction for word embeddings"
    - "all but the top simple and effective postprocessing for word representations"
    - "compressing word embeddings via deep compositional code learning"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-7 (induced by source paper "pytorch biggraph a large scale graph embedding system"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "mile a multi level framework for scalable graph embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "starspace embed all the things"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-8 (induced by source paper "assessing the lexico semantic relational knowledge captured by word and concept embeddings"):
  with members:
    - "not just about size a study on the role of distributed word representations in the analysis of scientific publications"
    - "a decomposable attention model for natural language inference"
    - "enriching word vectors with subword information"
    - "path based vs distributional information in recognizing lexical semantic relations"
    - "proje embedding projection for knowledge graph completion"
    - "visualizing and understanding neural models in nlp"
    - "experiments with three approaches to recognizing lexical entailment"
    - "on the properties of neural machine translation encoder decoder approaches"

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)