human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "link prediction based on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can graph neural networks count substructures")
  Member "dual convolutional neural network for graph of graphs link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non parametric learning of gaifman models")
  Member "hierarchical graph representation learning with differentiable pooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "temporally evolving community detection and prediction in content centric networks":
    also connected through other hyperedges:
      (none)
  Member "graphrnn generating realistic graphs with deep auto regressive models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "temporally evolving community detection and prediction in content centric networks":
    also connected through other hyperedges:
      (none)
  Member "representation learning on graphs methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to segment 3d point clouds in 2d image space")

Question: Please tell me which class the source paper 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)