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: "representation learning for attributed multiplex heterogeneous network"
  Abstract: Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p

Connected through hyperedge HE-1 (induced by source paper "magnn metapath aggregated graph neural network for heterogeneous graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
    - "position aware graph neural networks"
    - "knowledge graph convolutional networks for recommender systems"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-2 (induced by source paper "tripartite heterogeneous graph propagation for large scale social recommendation"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "applying deep learning to airbnb search"
    - "representation learning on graphs with jumping knowledge networks"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"
    - "a unified framework for community detection and network representation learning"

Connected through hyperedge HE-4 (induced by source paper "deeptrax embedding graphs of financial transactions"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "pytorch biggraph a large scale graph embedding system"
    - "graph convolutional neural networks for web scale recommender systems"
    - "large scale learnable graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "struc2vec learning node representations from structural identity"
    - "watch your step learning node embeddings via graph attention"

Connected through hyperedge HE-5 (induced by source paper "mobile app user attribute prediction by heterogeneous information network modeling"):
  with members:
    - "fine grained event categorization with heterogeneous graph convolutional networks"
    - "hetespaceywalk a heterogeneous spacey random walk for heterogeneous information network embedding"

Connected through hyperedge HE-6 (induced by source paper "mvn2vec preservation and collaboration in multi view network embedding"):
  with members:
    - "a structural representation learning for multi relational networks"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "don t walk skip online learning of multi scale network 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.SI(Social and Information Networks)