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: "from hashing to cnns training binaryweight networks via hashing"
  Abstract: Deep convolutional neural networks (CNNs) have shown appealing performance on various computer vision tasks in recent years. This motivates people to deploy CNNs to realworld applications. However, most of state-of-art CNNs require large memory and computational resources, which hinders the deployment on mobile devices. Recent studies show that low-bit weight representation can reduce much storage and memory demand, and also can achieve efficient network inference. To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing. In this paper, we first reveal the strong connection between inner-product preserving hashing and binary weight networks, and show that training binary weight networks can be intrinsically regarded as a hashing problem. Based on this perspective, we propose an alternating optimization method to learn the hash codes instead of directly learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by a large margin.

Connected through hyperedge HE-1 (induced by source paper "bringing giant neural networks down to earth with unlabeled data"):
  with members:
    - "importance estimation for neural network pruning"
    - "deep predictive coding network for object recognition"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "rethinking the value of network pruning"
    - "dynamic channel pruning feature boosting and suppression"

Connected through hyperedge HE-2 (induced by source paper "full stack optimization for accelerating cnns with fpga validation"):
  with members:
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "pact parameterized clipping activation for quantized neural networks"
    - "block sparse recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-3 (induced by source paper "accurate and compact convolutional neural networks with trained binarization"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "loss aware weight quantization of deep networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning accurate low bit deep neural networks with stochastic quantization"
    - "deep learning with low precision by half wave gaussian quantization"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "adaptive loss aware quantization for multi bit networks"):
  with members:
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "relaxed quantization for discretized neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-5 (induced by source paper "ir net forward and backward information retention for highly accurate binary neural networks"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "regularizing activation distribution for training binarized deep networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"

Connected through hyperedge HE-6 (induced by source paper "a targeted acceleration and compression framework for low bit neural networks"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "towards accurate binary convolutional neural network"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-7 (induced by source paper "a comparative study of neural network compression"):
  with members:
    - "pruned and structurally sparse neural networks"
    - "towards a theoretical understanding of hashing based neural nets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "predicting parameters in deep learning"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-8 (induced by source paper "recent advances in efficient computation of deep convolutional neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "exploring the regularity of sparse structure in convolutional neural networks"

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)