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: "ktan knowledge transfer adversarial network"
  Abstract: To reduce the large computation and storage cost of a deep convolutional neural network, the knowledge distillation based methods have pioneered to transfer the generalization ability of a large (teacher) deep network to a light-weight (student) network. However, these methods mostly focus on transferring the probability distribution of the softmax layer in a teacher network and thus neglect the intermediate representations. In this paper, we propose a knowledge transfer adversarial network to better train a student network. Our technique holistically considers both intermediate representations and probability distributions of a teacher network. To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained. Specifically, we arrange a Teacher-to-Student layer for enabling our framework suitable for various student structures. The intermediate representation helps the student network better understand the transferred generalization as compared to the probability distribution only. Furthermore, we infuse an adversarial learning process by employing a discriminator network, which can fully exploit the spatial correlation of feature maps in training a student network. The experimental results demonstrate that the proposed method can significantly improve the performance of a student network on both image classification and object detection tasks.

Connected through hyperedge HE-1 (induced by source paper "feature map level online adversarial knowledge distillation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "federated learning challenges methods and future directions"
    - "paraphrasing complex network network compression via factor transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "deep mutual learning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "connecting generative adversarial networks and actor critic methods"

Connected through hyperedge HE-2 (induced by source paper "be your own teacher improve the performance of convolutional neural networks via self distillation"):
  with members:
    - "slimmable neural networks"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "meal multi model ensemble via adversarial learning"
    - "label refinery improving imagenet classification through label progression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "focal loss for dense object detection"
    - "deep mutual learning"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-3 (induced by source paper "knowledge squeezed adversarial network compression"):
  with members:
    - "model compression via distillation and quantization"
    - "attention guided answer distillation for machine reading comprehension"
    - "learn to pay attention"
    - "adversarial network compression"
    - "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-4 (induced by source paper "memnet memory efficiency guided neural architecture search with augment trim learning"):
  with members:
    - "dvolver efficient pareto optimal neural network architecture search"
    - "you only search once single shot neural architecture search via direct sparse optimization"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "wearable audio and imu based shot detection in racquet sports"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"

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)