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: "review on graph feature learning and feature extraction techniques for link prediction"
  Abstract: Studying networks to predict the emerging interactions is a common research problem for both fields of network science and machine learning. The problem of predicting future or missing relationships in networks is called link prediction. Machine learning studies have mostly approached to this problem as a clustering or a classification task. A few obstacles might be involved in approaching network datasets through machine learning models, including undefined euclidean distance, extracting proper features, unbalanced data classes due to the sparsity of real networks, or embedding graphs to a low dimensional vector space while preserving the structure to study networks. Extensive studies have examined these problems from different aspects and proposed methods some of which might work very well for a specific application but not as a global solution. In this survey, we review the general-purpose techniques at the heart of link prediction problem, which can be combined with domain-specific heuristic methods in practice. To the best of our knowledge, this survey is the first comprehensive study which considers all of the mentioned challenges about studying networks and approaching them through machine learning models. We provide a diverse study on feature extraction techniques for network datasets based on similarity metrics, maximum likelihood methods, probabilistic methods and graph representation learning. Our other contributions include proposing a taxonomy to classify link prediction methods and continue with introducing valuable network dataset collections to study the problem of link prediction. Our final contribution is discussing and proposing a few models, including a multi-stream feature learning model to exploit the benefits of local and quasi-local network extraction techniques combined with graph representation learning.

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)