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: "node classification in social networks"
  Abstract: When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled. These labels can indicate demographic values, interest, beliefs or other characteristics of the nodes (users). A core problem is to use this information to extend the labeling so that all nodes are assigned a label (or labels).

Connected through hyperedge HE-1 (induced by source paper "simple and effective graph autoencoders with one hop linear models"):
  with members:
    - "rwr gae random walk regularization for graph auto encoders"
    - "a degeneracy framework for scalable graph autoencoders"
    - "keep it simple graph autoencoders without graph convolutional networks"
    - "effective decoding in graph auto encoder using triadic closure"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"

Connected through hyperedge HE-2 (induced by source paper "factorized graph representations for semi supervised learning from sparse data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "a general framework for estimating graphlet statistics via random walk"
    - "semi supervised classification with graph convolutional networks"
    - "graph based semi supervised learning for relational networks"
    - "semi supervised learning with heterophily"
    - "localization and centrality in networks"
    - "linearized and single pass belief propagation"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "mile a multi level framework for scalable graph embedding"

Connected through hyperedge HE-4 (induced by source paper "network transfer learning via adversarial domain adaptation with graph convolution"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "beyond sharing weights for deep domain adaptation"
    - "attributed graph clustering via adaptive graph convolution"
    - "network embedding for cross network node classification"
    - "graph neural networks for social recommendation"
    - "label efficient semi supervised learning via graph filtering"
    - "regal representation learning based graph alignment"

Connected through hyperedge HE-5 (induced by source paper "deep representation learning for social network analysis"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "techniques for interpretable machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "attributed social network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-6 (induced by source paper "aligraph a comprehensive graph neural network platform"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph networks as learnable physics engines for inference and control"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "attributed social network embedding"
    - "one shot relational learning for knowledge graphs"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"

Connected through hyperedge HE-7 (induced by source paper "deep graph representation learning"):
  with members:
    - "learning representations of graph data a survey"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "large scale learnable graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "predicting multicellular function through multi layer tissue networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-8 (induced by source paper "graph transformer networks"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "self attention graph pooling"
    - "graph wavelet neural network"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

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)