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: "netsmf large scale network embedding as sparse matrix factorization"
  Abstract: We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2) the explicit factorization of such matrix generates more powerful embeddings than existing methods. However, directly constructing and factorizing this matrix-which is dense-is prohibitively expensive in terms of both time and space, making it not scalable for large networks.    In this work, we present the algorithm of large-scale network embedding as sparse matrix factorization (NetSMF). NetSMF leverages theories from spectral sparsification to efficiently sparsify the aforementioned dense matrix, enabling significantly improved efficiency in embedding learning. The sparsified matrix is spectrally close to the original dense one with a theoretically bounded approximation error, which helps maintain the representation power of the learned embeddings. We conduct experiments on networks of various scales and types. Results show that among both popular benchmarks and factorization based methods, NetSMF is the only method that achieves both high efficiency and effectiveness. We show that NetSMF requires only 24 hours to generate effective embeddings for a large-scale academic collaboration network with tens of millions of nodes, while it would cost DeepWalk months and is computationally infeasible for the dense matrix factorization solution. The source code of NetSMF is publicly available1.

Connected through hyperedge HE-1 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank"):
  with members:
    - "adversarial training methods for network embedding"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "efficient algorithms for approximate single source personalized pagerank queries"
    - "pytorch biggraph a large scale graph embedding system"
    - "scalable graph embeddings via sparse transpose proximities"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-2 (induced by source paper "network representation learning consolidation and renewed bearing"):
  with members:
    - "pl nmf parallel locality optimized non negative matrix factorization"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mile a multi level framework for scalable graph embedding"
    - "verse versatile graph embeddings from similarity measures"
    - "fusion graph convolutional networks"
    - "systematic biases in link prediction comparing heuristic and graph embedding based methods"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"

Connected through hyperedge HE-3 (induced by source paper "representation learning for attributed multiplex heterogeneous network"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "attributed social network embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-4 (induced by source paper "exponential family graph embeddings"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "kernel node embeddings"
    - "a comprehensive survey on graph neural networks"
    - "tne a latent model for representation learning on networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "biasedwalk biased sampling for representation learning on graphs"
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"

Connected through hyperedge HE-5 (induced by source paper "fast and accurate network embeddings via very sparse random projection"):
  with members:
    - "a comparative study for unsupervised network representation learning"
    - "a comprehensive comparison of unsupervised network representation learning methods"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "billion scale network embedding with iterative random projection"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-6 (induced by source paper "kernel node embeddings"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "tne a latent model for representation learning on networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "biasedwalk biased sampling for representation learning on graphs"
    - "network representation learning a survey"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"

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.SI(Social and Information Networks)