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: "link prediction in complex networks a mutual information perspective"
  Abstract: Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

Connected through hyperedge HE-1 (induced by source paper "review on graph feature learning and feature extraction techniques for link prediction"):
  with members:
    - "link prediction based on graph neural networks"
    - "dual convolutional neural network for graph of graphs link prediction"
    - "hierarchical graph representation learning with differentiable pooling"
    - "temporally evolving community detection and prediction in content centric networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "temporally evolving community detection and prediction in content centric networks"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-2 (induced by source paper "investigating negative interactions in multiplex networks a mutual information approach"):
  with members:
    - "leveraging network dynamics for improved link prediction"
    - "signed link analysis in social media networks"
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "link prediction in social networks the state of the art"
    - "applying weighted pagerank to author citation networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-3 (induced by source paper "a blended deep learning approach for predicting user intended actions"):
  with members:
    - "deep learning in customer churn prediction unsupervised feature learning on abstract company independent feature vectors"
    - "why should i trust you explaining the predictions of any classifier"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "a neural network based approach for predicting customer churn in cellular network services"

Connected through hyperedge HE-4 (induced by source paper "improving local clustering based top l link prediction methods via asymmetric link clustering information"):
  with members:
    - "link prediction with node clustering coefficient"

Connected through hyperedge HE-5 (induced by source paper "node similarity distribution of complex networks and its application in link prediction"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "measure of node similarity in multilayer 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)