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: "predicting user roles in social networks using transfer learning with feature transformation"
  Abstract: How can we recognise social roles of people, given a completely unlabelled social network? We may train a role classification algorithm on another dataset, but then that dataset may have largely different values of its features, for instance, the degrees in the other network may be distributed in a completely different way than in the first network. Thus, a way to transfer the features of different networks to each other or to a common feature space is needed. This type of setting is called transfer learning. In this paper, we present a transfer learning approach to network role classification based on feature transformations from each network's local feature distribution to a global feature space. We implement our approach and show experiments on real-world networks of discussions on Wikipedia as well as online forums. We also show a concrete application of our approach to an enterprise use case, where we predict the user roles in ARIS Community, the online platform for customers of Software AG, the second-largest German software vendor. Evaluation results show that our approach is suitable for transferring knowledge of user roles across networks.

Connected through hyperedge HE-1 (induced by source paper "a quotient space formulation for statistical analysis of graphical data"):
  with members:
    - "unsupervised inductive graph level representation learning via graph graph proximity"
    - "harp hierarchical representation learning for networks"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "constant state of change engagement inequality in temporal dynamic networks"):
  with members:
    - "motifs in temporal networks"
    - "role discovery in networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-3 (induced by source paper "understanding social networks using transfer learning"):
  with members:
    - "the slashdot zoo mining a social network with negative edges"

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)