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 social networks the state of the art"
  Abstract: In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

Connected through hyperedge HE-1 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "graph embedding techniques applications and performance a survey"
    - "a hybrid model for identity obfuscation by face replacement"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"

Connected through hyperedge HE-2 (induced by source paper "graph convolutional gaussian processes for link prediction"):
  with members:
    - "link prediction based on graph neural networks"
    - "pitfalls of graph neural network evaluation"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "tie strength in online social networks and its applications a brief study"):
  with members:
    - "detecting strong ties using network motifs"
    - "link classification and tie strength ranking in online social networks with exogenous interaction networks"
    - "routing in mobile ad hoc networks using social tie strengths and mobility plans"

Connected through hyperedge HE-4 (induced by source paper "gravity inspired graph autoencoders for directed link prediction"):
  with members:
    - "a degeneracy framework for scalable graph autoencoders"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "simplifying graph convolutional networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning to make predictions on graphs with autoencoders"

Connected through hyperedge HE-5 (induced by source paper "meta graph few shot link prediction via meta learning"):
  with members:
    - "learning to propagate for graph meta learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "link prediction based on graph neural networks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "visual reasoning with multi hop feature modulation"

Connected through hyperedge HE-6 (induced by source paper "subgraph networks with application to structural feature space expansion"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "motifs in temporal networks"
    - "detecting strong ties using network motifs"
    - "distributed representation of subgraphs"
    - "graph2vec learning distributed representations of graphs"
    - "wide deep learning for recommender systems"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "privacy attacks on network embeddings"):
  with members:
    - "towards demystifying membership inference attacks"
    - "graph embedding techniques applications and performance a survey"
    - "algorithms that remember model inversion attacks and data protection law"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "sok security and privacy in machine learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional 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)