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: "adversarial network compression"
  Abstract: Neural network compression has recently received much attention due to the computational requirements of modern deep models. In this work, our objective is to transfer knowledge from a deep and accurate model to a smaller one. Our contributions are threefold: (i) we propose an adversarial network compression approach to train the small student network to mimic the large teacher, without the need for labels during training; (ii) we introduce a regularization scheme to prevent a trivially-strong discriminator without reducing the network capacity and (iii) our approach generalizes on different teacher-student models. #R##N#In an extensive evaluation on five standard datasets, we show that our student has small accuracy drop, achieves better performance than other knowledge transfer approaches and it surpasses the performance of the same network trained with labels. In addition, we demonstrate state-of-the-art results compared to other compression strategies.

Connected through hyperedge HE-1 (induced by source paper "residual knowledge distillation"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "an embarrassingly simple approach for knowledge distillation"
    - "self supervised knowledge distillation using singular value decomposition"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "n2n learning network to network compression via policy gradient reinforcement learning"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

Connected through hyperedge HE-2 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-3 (induced by source paper "knowledge distillation for end to end person search"):
  with members:
    - "learning context graph for person search"
    - "query guided end to end person search"
    - "person search by multi scale matching"
    - "model compression via distillation and quantization"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "person search via a mask guided two stream cnn model"

Connected through hyperedge HE-4 (induced by source paper "learning with privileged information via adversarial discriminative modality distillation"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "modality distillation with multiple stream networks for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "class dependent compression of deep neural networks"):
  with members:
    - "knowledge squeezed adversarial network compression"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "class balanced loss based on effective number of samples"
    - "darc differentiable architecture compression"
    - "compressing gans using knowledge distillation"

Connected through hyperedge HE-6 (induced by source paper "knowledge squeezed adversarial network compression"):
  with members:
    - "ktan knowledge transfer adversarial network"
    - "model compression via distillation and quantization"
    - "attention guided answer distillation for machine reading comprehension"
    - "learn to pay attention"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-7 (induced by source paper "dbp discrimination based block level pruning for deep model acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "similarity preserving knowledge distillation"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "greedy layerwise learning can scale to imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-8 (induced by source paper "basisconv a method for compressed representation and learning in cnns"):
  with members:
    - "a survey of model compression and acceleration for deep neural networks"
    - "densely connected convolutional networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "quantized convolutional neural networks for mobile devices"
    - "data free parameter pruning for deep 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)