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: "parallelizing word2vec in shared and distributed memory"
  Abstract: Word2Vec is a widely used algorithm for extracting low-dimensional vector representations of words. It generated considerable excitement in the machine learning and natural language processing (NLP) communities recently due to its exceptional performance in many NLP applications such as named entity recognition, sentiment analysis, machine translation and question answering. State-of-the-art algorithms including those by Mikolov et al. have been parallelized for multi-core CPU architectures but are based on vector-vector operations that are memory-bandwidth intensive and do not efficiently use computational resources. In this paper, we improve reuse of various data structures in the algorithm through the use of minibatching, hence allowing us to express the problem using matrix multiply operations. We also explore different techniques to distribute word2vec computation across nodes in a compute cluster, and demonstrate good strong scalability up to 32 nodes. In combination, these techniques allow us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge.

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"
    - "network efficient distributed word2vec training system for large vocabularies"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (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"
    - "network efficient distributed word2vec training system for large vocabularies"
    - "distributed representations for biological sequence analysis"
    - "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-3 (induced by source paper "lasagne locality and structure aware graph node embedding"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "neural message passing for quantum chemistry"
    - "parallel local graph clustering"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "on sampling strategies for neural network based collaborative filtering"):
  with members:
    - "joint text embedding for personalized content based recommendation"
    - "leveraging visual question answering for image caption ranking"
    - "entity embedding based anomaly detection for heterogeneous categorical events"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "a neural autoregressive approach to collaborative filtering"
    - "character level convolutional networks for text classification"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "efficient parallel learning of word2vec"):
  with members:
    - "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.DC(Distributed, Parallel, and Cluster Computing)