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: "multimodal controller for generative models"
  Abstract: Class-conditional generative models are crucial tools for data generation from user-specified class labels. A number of existing approaches for class-conditional generative models require nontrivial modifications of existing architectures, in order to model conditional information fed into the model. In this paper, we introduce a new method called multimodal controller to generate multimodal data without introducing additional model parameters. With the proposed technique, the model can be trained easily from non-conditional generative models by simply attaching controllers at each layer. Each controller grants label-specific model parameters. Thus the proposed method does not require additional model complexity. In the absence of the controllers, our model reduces to non-conditional generative models. Numerical experiments demonstrate the effectiveness of our proposed method in comparison with those of the existing non-conditional and conditional generative models. Additionally, our numerical results demonstrate that a small portion (10%) of label-specific model parameters is required to generate class-conditional MNIST and FashionMNIST images.

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.LG(Machine Learning)