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: "heterogeneous information network embedding for meta path based proximity"
  Abstract: A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity between vertices in the original graph. This way, typical search and mining methods can be applied in the embedded space with the help of off-the-shelf multidimensional indexing approaches. Existing network embedding techniques focus on homogeneous networks, where all vertices are considered to belong to a single class.

Connected through hyperedge HE-1 (induced by source paper "deep representation learning for social network analysis"):
  with members:
    - "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"
    - "techniques for interpretable machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "attributed social network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-2 (induced by source paper "attentive knowledge graph embedding for personalized recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "graph neural networks for social recommendation"
    - "knowledge graph convolutional networks for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "community detection in node attributed social networks a survey"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "non linear attributed graph clustering by symmetric nmf with pu learning"
    - "efficiently clustering very large attributed graphs"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-4 (induced by source paper "hahe hierarchical attentive heterogeneous information network embedding"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "attention models in graphs a survey"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "node classification framework"):
  with members:
    - "learning structural node embeddings via diffusion wavelets"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "geometric deep learning going beyond euclidean data"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "similarity modeling on heterogeneous networks via automatic path discovery"):
  with members:
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "neural combinatorial optimization with reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "meta graph based hin spectral embedding methods analyses and insights"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "multi view graph embedding with hub detection for brain network analysis"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (induced by source paper "a graph auto encoder for attributed network embedding"):
  with members:
    - "learning structural node embeddings via diffusion wavelets"
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "geometric deep learning going beyond euclidean data"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "line large scale information network embedding"

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)