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: "enhanced network embeddings via exploiting edge labels"
  Abstract: Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these methods treat the relations between nodes as a binary variable and ignore the rich semantics of edges. In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes. Experiments on several real-world networks illustrate that by considering different relations between different node pairs, our method is capable of producing node embeddings of higher quality than a number of state-of-the-art network embedding methods, as evaluated on a challenging multi-label node classification task.

Connected through hyperedge HE-1 (induced by source paper "epine enhanced proximity information network embedding"):
  with members:
    - "network representation learning a survey"
    - "node2vec scalable feature learning for networks"
    - "don t walk skip online learning of multi scale network embeddings"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach"):
  with members:
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph edit distance computation via graph neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "representation learning on graphs methods and applications"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (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"
    - "a tutorial on network embeddings"
    - "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"

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)