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: "mile a multi level framework for scalable graph embedding"
  Abstract: Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework -- a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a novel graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while also often generating embeddings of better quality for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation.

Connected through hyperedge HE-1 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "attention models in graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "graphzoom a multi level spectral approach for accurate and scalable graph embedding"):
  with members:
    - "network embedding on compression and learning"
    - "initialization for network embedding a graph partition approach"
    - "learning topological representation for networks via hierarchical sampling"
    - "label efficient semi supervised learning via graph filtering"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph embedding techniques applications and performance a survey"
    - "cosine compressive network embedding on large scale information networks"

Connected through hyperedge HE-3 (induced by source paper "network representation learning consolidation and renewed bearing"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "pl nmf parallel locality optimized non negative matrix factorization"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "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-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"
    - "a tutorial on network embeddings"
    - "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 "hierarchical graph convolutional networks for semi supervised node classification"):
  with members:
    - "session based recommendation with graph neural network"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "hierarchical graph representation learning with differentiable pooling"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "large scale learnable graph convolutional networks"
    - "harp hierarchical representation learning for networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-6 (induced by source paper "pytorch biggraph a large scale graph embedding system"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "starspace embed all the things"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"
    - "swivel improving embeddings by noticing what s missing"
    - "semi supervised classification with graph convolutional networks"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-7 (induced by source paper "parallel computation of graph embeddings"):
  with members:
    - "simplifying graph convolutional networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "the global anchor method for quantifying linguistic shifts and domain adaptation"
    - "on the dimensionality of word embedding"
    - "representation learning on graphs methods and applications"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (induced by source paper "cosine compressive network embedding on large scale information networks"):
  with members:
    - "word2bits quantized word vectors"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "compressing word embeddings via deep compositional code learning"
    - "natural language processing with small feed forward networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

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.AI(Artificial Intelligence)