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 framework"
  Abstract: GCN is a recent effective algorithm which effectively learns a function incorporate both graph structure and node features for semisupervised graph based node classification. Although GCN exceeds other state-of-the-art methods, the number of parameters that need to be learned are still significantly more than the number of used samples. Because only labeled nodes and their 2-hop neighbors are used for learning parameters in GCN. As a consequence the accuracy of node classification declines sharply when decreasing the size of train data. In order to reducing parameters and making better use of unlabeled nodes, we proposes to use unsupervised representation algorithm such as autoencoder to pretrain a embedding for each node, which captures effective information from input features and reduce parameters in graph convolution layers. Experiments show that our model has increased by 0.87%, 1.96%, 1.87% compared to baseline on Cora, Citeseer and Pubmed data respectively. The performance of our model declines slower than GCN when decreasing the train samples.

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)