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: "low cost transfer learning of face tasks"
  Abstract: Do we know what the different filters of a face network#R##N#represent? Can we use this filter information to train other#R##N#tasks without transfer learning? For instance, can age, head#R##N#pose, emotion and other face related tasks be learned from#R##N#face recognition network without transfer learning? Understanding the role of these filters allows us to transfer#R##N#knowledge across tasks and take advantage of large data#R##N#sets in related tasks. Given a pretrained network, we can#R##N#infer which tasks the network generalizes for and the best#R##N#way to transfer the information to a new task.#R##N#We demonstrate a computationally inexpensive algorithm to reuse the filters of a face network for a task it was#R##N#not trained for. Our analysis proves these attributes can be#R##N#extracted with an accuracy comparable to what is obtained#R##N#with transfer learning, but 10 times faster. We show that the#R##N#information about other tasks is present in relatively small#R##N#number of filters. We use these insights to do task specific#R##N#pruning of a pretrained network. Our method gives significant compression ratios with reduction in size of 95% and#R##N#computational reduction of 60%

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.CV(Computer Vision and Pattern Recognition)