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: "learning sentimental influences from users behaviors"
  Abstract: Modeling interpersonal influence on different sentimental polarities is a fundamental problem in opinion formation and viral marketing. There has not been seen an effective solution for learning sentimental influences from users' behaviors yet. Previous related works on information propagation directly define interpersonal influence between each pair of users as a parameter, which is independent from each others, even if the influences come from or affect the same user. And influences are learned from user's propagation behaviors, namely temporal cascades, while sentiments are not associated with them. Thus we propose to model the interpersonal influence by latent influence and susceptibility matrices defined on individual users and sentiment polarities. Such low-dimensional and distributed representations naturally make the interpersonal influences related to the same user coupled with each other, and in turn, reduce the model complexity. Sentiments act on different rows of parameter matrices, depicting their effects in modeling cascades. With the iterative optimization algorithm of projected stochastic gradient descent over shuffled mini-batches and Adadelta update rule, negative cases are repeatedly sampled with the distribution of infection frequencies users, for reducing computation cost and optimization imbalance. Experiments are conducted on Microblog dataset. The results show that our model achieves better performance than the state-of-the-art and pair-wise models. Besides, analyzing the distribution of learned users' sentimental influences and susceptibilities results some interesting discoveries.

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)