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: "influence maximization via representation learning"
  Abstract: Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges lie on the one hand on the computational demand of the algorithmic solution which restricts the scalability, and on the other the quality of the predicted influence spread. In this work, we propose IMINFECTOR (Influence Maximization with INFluencer vECTORs), a method that aspires to address both problems using representation learning. It comprises of two parts. The first is based on a multi-task neural network that uses logs of diffusion cascades to embed diffusion probabilities between nodes as well as the ability of a node to create massive cascades. The second part uses diffusion probabilities to reformulate influence maximization as a weighted bipartite matching problem and capitalizes on the learned representations to find a seed set using a greedy heuristic approach. We apply our method in three sizable networks accompanied by diffusion cascades and evaluate it using unseen diffusion cascades from future time steps. We observe that our method outperforms various competitive algorithms and metrics from the diverse landscape of influence maximization, in terms of prediction precision and seed set quality.

Connected through hyperedge HE-1 (induced by source paper "learning policies for social network discovery with reinforcement learning"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "semi supervised classification with graph convolutional networks"
    - "prioritized experience replay"
    - "deepwalk online learning of social representations"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)