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: "compressing low precision deep neural networks using sparsity induced regularization in ternary networks"
  Abstract: A low precision deep neural network training technique for producing sparse, ternary neural networks is presented. The technique incorporates hard- ware implementation costs during training to achieve significant model compression for inference. Training involves three stages: network training using L2 regularization and a quantization threshold regularizer, quantization pruning, and finally retraining. Resulting networks achieve improved accuracy, reduced memory footprint and reduced computational complexity compared with conventional methods, on MNIST and CIFAR10 datasets. Our networks are up to 98% sparse and 5 & 11 times smaller than equivalent binary and ternary models, translating to significant resource and speed benefits for hardware implementations.

Connected through hyperedge HE-1 (induced by source paper "syq learning symmetric quantization for efficient deep neural networks"):
  with members:
    - "deep learning with low precision by half wave gaussian quantization"
    - "scaling binarized neural networks on reconfigurable logic"
    - "ternary neural networks with fine grained quantization"
    - "finn a framework for fast scalable binarized neural network inference"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "accelerating deep convolutional networks using low precision and sparsity"

Connected through hyperedge HE-2 (induced by source paper "finn r an end to end deep learning framework for fast exploration of quantized neural networks"):
  with members:
    - "accuracy to throughput trade offs for reduced precision neural networks on reconfigurable logic"
    - "streaming architecture for large scale quantized neural networks on an fpga based dataflow platform"
    - "in datacenter performance analysis of a tensor processing unit"
    - "wrpn wide reduced precision networks"
    - "generic and universal parallel matrix summation with a flexible compression goal for xilinx fpgas"
    - "streamlined deployment for quantized neural networks"
    - "deep learning with low precision by half wave gaussian quantization"
    - "tactics to directly map cnn graphs on embedded fpgas"

Connected through hyperedge HE-3 (induced by source paper "block sparse recurrent neural networks"):
  with members:
    - "learning intrinsic sparse structures within long short term memory"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "exploring sparsity in recurrent neural networks"
    - "learning structured sparsity in deep neural networks"
    - "exploring the limits of language modeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-4 (induced by source paper "tflms large model support in tensorflow by graph rewriting"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "universal deep neural network compression"
    - "identity mappings in deep residual networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "training deep nets with sublinear memory cost"
    - "deep residual learning for image recognition"

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)