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: "semi relaxed quantization with dropbits training low bit neural networks via bit wise regularization"
  Abstract: Neural Network quantization, which aims to reduce bit-lengths of the network weights and activations, is one of the key ingredients to reduce the size of neural networks for their deployments to resource-limited devices. However, compressing to low bit-lengths may incur large loss of information and preserving the performance of the full-precision networks under these settings is extremely challenging even with the state-of-the-art quantization approaches. To tackle this problem of low-bit quantization, we propose a novel Semi-Relaxed Quantization (SRQ) that can effectively reduce the quantization error, along with a new regularization technique, DropBits which replaces dropout regularization to randomly drop the bits instead of neurons to minimize information loss while improving generalization on low-bit networks. Moreover, we show the possibility of learning heterogeneous quantization levels, that finds proper bit-lengths for each layer using DropBits. We experimentally validate our method on various benchmark datasets and network architectures, whose results show that our method largely outperforms recent quantization approaches. To the best of our knowledge, we are the first in obtaining competitive performance on 3-bit quantization of ResNet-18 on ImageNet dataset with both weights and activations quantized, across all layers. Last but not the least, we show promising results on heterogeneous quantization, which we believe will open the door to new research directions in neural network quantization.

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)