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: "a survey on network embedding"
  Abstract: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.

Connected through hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-2 (induced by source paper "learning to hash with graph neural networks for recommender systems"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deep representation learning for social network analysis"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-3 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"

Connected through hyperedge HE-4 (induced by source paper "graph ordering towards the optimal by learning"):
  with members:
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "verse versatile graph embeddings from similarity measures"
    - "deep sets"
    - "representation learning over dynamic graphs"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "community aware random walk for network embedding"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "dynamic graph representation learning via self attention networks"
    - "attributed social network embedding"
    - "network representation learning a survey"

Connected through hyperedge HE-6 (induced by source paper "debayes a bayesian method for debiasing network embeddings"):
  with members:
    - "monet debiasing graph embeddings via the metadata orthogonal training unit"
    - "a survey on bias and fairness in machine learning"
    - "compositional fairness constraints for graph embeddings"
    - "exploring author gender in book rating and recommendation"
    - "representation learning on graphs methods and applications"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "rne a scalable network embedding for billion scale recommendation"):
  with members:
    - "billion scale network embedding with iterative random projection"
    - "neural collaborative filtering"
    - "billion scale similarity search with gpus"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "piecewise linear regressions for approximating distance metrics"):
  with members:
    - "robust geometric spanners"

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)