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: "network efficient distributed word2vec training system for large vocabularies"
  Abstract: Word2vec is a popular family of algorithms for unsupervised training of dense vector representations of words on large text corpuses. The resulting vectors have been shown to capture semantic relationships among their corresponding words, and have shown promise in reducing a number of natural language processing (NLP) tasks to mathematical operations on these vectors. While heretofore applications of word2vec have centered around vocabularies with a few million words, wherein the vocabulary is the set of words for which vectors are simultaneously trained, novel applications are emerging in areas outside of NLP with vocabularies comprising several 100 million words. Existing word2vec training systems are impractical for training such large vocabularies as they either require that the vectors of all vocabulary words be stored in the memory of a single server or suffer unacceptable training latency due to massive network data transfer. In this paper, we present a novel distributed, parallel training system that enables unprecedented practical training of vectors for vocabularies with several 100 million words on a shared cluster of commodity servers, using far less network traffic than the existing solutions. We evaluate the proposed system on a benchmark dataset, showing that the quality of vectors does not degrade relative to non-distributed training. Finally, for several quarters, the system has been deployed for the purpose of matching queries to ads in Gemini, the sponsored search advertising platform at Yahoo, resulting in significant improvement of business metrics.

Connected through hyperedge HE-1 (induced by source paper "netsmf large scale network embedding as sparse matrix factorization"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "representation learning on graphs methods and applications"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "deeptrax embedding graphs of financial transactions"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "pytorch biggraph a large scale graph embedding system"
    - "representation learning for attributed multiplex heterogeneous network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "large scale learnable graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "struc2vec learning node representations from structural identity"

Connected through hyperedge HE-3 (induced by source paper "distributed word2vec using graph analytics frameworks"):
  with members:
    - "code2vec learning distributed representations of code"
    - "code2seq generating sequences from structured representations of code"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed representations for biological sequence analysis"
    - "parallelizing word2vec in shared and distributed memory"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-4 (induced by source paper "graph embeddings at scale"):
  with members:
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "starspace embed all the things"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "node2vec scalable feature learning for networks"
    - "e commerce in your inbox product recommendations at scale"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "asynchronous training of word embeddings for large text corpora"):
  with members:
    - "efficient parallel learning of word2vec"
    - "enriching word vectors with subword information"
    - "an ensemble method to produce high quality word embeddings"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

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)