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: "quantized memory augmented neural networks"
  Abstract: Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intriguing AI tasks that would otherwise be hard to address. This paper concerns the problem of quantizing MANNs. Quantization is known to be effective when we deploy deep models on embedded systems with limited resources. Furthermore, quantization can substantially reduce the energy consumption of the inference procedure. These benefits justify recent developments of quantized multi layer perceptrons, convolutional networks, and RNNs. However, no prior work has reported the successful quantization of MANNs. The in-depth analysis presented here reveals various challenges that do not appear in the quantization of the other networks. Without addressing them properly, quantized MANNs would normally suffer from excessive quantization error which leads to degraded performance. In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge. In our experiments, we achieved a computation-energy gain of 22x with 8-bit fixed-point and binary quantization compared to the floating-point implementation. Measured on the bAbI dataset, the resulting model, named the quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit fixed-point and binary quantization, respectively, compared to the MANN quantized using conventional techniques.

Connected through hyperedge HE-1 (induced by source paper "spiking yolo spiking neural network for energy efficient object detection"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "fast and efficient information transmission with burst spikes in deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "direct training for spiking neural networks faster larger better"
    - "long short term memory and learning to learn in networks of spiking neurons"

Connected through hyperedge HE-2 (induced by source paper "making good on lstms unfulfilled promise"):
  with members:
    - "exploring interpretable lstm neural networks over multi variable data"
    - "continual learning augmented investment decisions"
    - "unseen class discovery in open world classification"
    - "prototypical networks for few shot learning"
    - "encoder based lifelong learning"
    - "learning to remember rare events"
    - "gradient episodic memory for continual learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-3 (induced by source paper "energy efficient inference accelerator for memory augmented neural networks on an fpga"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "accelerated training for massive classification via dynamic class selection"
    - "hardware automated datafow deployment of cnns"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "finn a framework for fast scalable binarized neural network inference"
    - "an efficient approach to boosting performance of deep spiking network training"
    - "fpga based low power speech recognition with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "fast and efficient information transmission with burst spikes in deep spiking neural networks"):
  with members:
    - "the impact of on chip communication on memory technologies for neuromorphic systems"
    - "streaming mann a streaming based inference for energy efficient memory augmented neural networks"
    - "energy efficient inference accelerator for memory augmented neural networks on an fpga"
    - "an efficient approach to boosting performance of deep spiking network training"
    - "eie efficient inference engine on compressed deep neural network"
    - "training deep spiking neural networks using backpropagation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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)