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 tutorial on network embeddings"
  Abstract: Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.

Connected through hyperedge HE-1 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netlsd hearing the shape of a graph"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"
    - "on the dimensionality of word embedding"

Connected through hyperedge HE-2 (induced by source paper "a capsule network based model for learning node embeddings"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "relational inductive biases deep learning and graph networks"
    - "spine structural identity preserved inductive network embedding"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "network representation learning a survey"
    - "dynamic routing between capsules"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-3 (induced by source paper "monet debiasing graph embeddings via the metadata orthogonal training unit"):
  with members:
    - "global vectors for node representations"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "compositional fairness constraints for graph embeddings"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "enhancing network embedding with auxiliary information an explicit matrix factorization perspective"
    - "a survey on network embedding"
    - "the ground truth about metadata and community detection in networks"

Connected through hyperedge HE-4 (induced by source paper "initialization for network embedding a graph partition approach"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mile a multi level framework for scalable graph embedding"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-5 (induced by source paper "network embedding on compression and learning"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "atp directed graph embedding with asymmetric transitivity preservation"
    - "network representation learning a survey"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"

Connected through hyperedge HE-6 (induced by source paper "is a single embedding enough learning node representations that capture multiple social contexts"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "enhanced network embeddings via exploiting edge labels"
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"
    - "harp hierarchical representation learning for networks"
    - "learning edge representations via low rank asymmetric projections"
    - "watch your step learning node embeddings via graph attention"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (induced by source paper "geometric laplacian eigenmap embedding"):
  with members:
    - "the simplex geometry of graphs"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "representation learning on graphs methods and applications"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "improving question answering by commonsense based pre training"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "think you have solved question answering try arc the ai2 reasoning challenge"

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)