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: "high performance binarized neural networks trained on the imagenet classification task"
  Abstract: We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet LSVRC-2102 dataset classification task and achieved a good performance. With a moderate size network of 10 layers, we obtained top-5 classification accuracy rate of 81 percent on validation set which is much better than previous published results. We expect training networks of a much better performance through increase network depth would be straight forward by following our current strategies. A detailed discussion on strategies used in the network training is included as well as preliminary analysis.

Connected through hyperedge HE-1 (induced by source paper "extreme language model compression with optimal subwords and shared projections"):
  with members:
    - "q bert hessian based ultra low precision quantization of bert"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "cross lingual language model pretraining"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "fq conv fully quantized convolution for efficient and accurate inference"):
  with members:
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "deep neural network compression with single and multiple level quantization"
    - "label refinery improving imagenet classification through label progression"
    - "deep residual learning for small footprint keyword spotting"
    - "the high dimensional geometry of binary neural networks"
    - "hello edge keyword spotting on microcontrollers"

Connected through hyperedge HE-3 (induced by source paper "what does it mean to understand a neural network"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "synthesizing robust adversarial examples"
    - "distilling a neural network into a soft decision tree"
    - "binarized neural networks on the imagenet classification task"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-4 (induced by source paper "yodann an architecture for ultra low power binary weight cnn acceleration"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural networks"
    - "dialog based language learning"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "energy efficient convnets through approximate computing"

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)