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: "wordrank learning word embeddings via robust ranking"
  Abstract: Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a ranking problem due to the ranking nature of the evaluation metrics. Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses. The performance of WordRank is measured in word similarity and word analogy benchmarks, and the results are compared to the state-of-the-art word embedding techniques. Our algorithm is very competitive to the state-of-the- arts on large corpora, while outperforms them by a significant margin when the training set is limited (i.e., sparse and noisy). With 17 million tokens, WordRank performs almost as well as existing methods using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node distributed implementation of WordRank is publicly available for general usage.

Connected through hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "parallax visualizing and understanding the semantics of embedding spaces via algebraic formulae"):
  with members:
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "starspace embed all the things"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "item2vec neural item embedding for collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "semantics derived automatically from language corpora necessarily contain human biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"

Connected through hyperedge HE-3 (induced by source paper "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"):
  with members:
    - "confidence based graph convolutional networks for semi supervised learning"
    - "dating documents using graph convolution networks"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "graph convolutional networks for text classification"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-4 (induced by source paper "learningword embeddings for low resource languages by pu learning"):
  with members:
    - "vqa visual question answering"
    - "pu learning for matrix completion"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "improving negative sampling for word representation using self embedded features"):
  with members:
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "dependency recurrent neural language models for sentence completion"
    - "neural programmer inducing latent programs with gradient descent"
    - "character aware neural language models"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "distributed representations of sentences and documents"

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)