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: "be your own teacher improve the performance of convolutional neural networks via self distillation"
  Abstract: Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices.Our codes will be released on github soon.

Connected through hyperedge HE-1 (induced by source paper "switchable precision neural networks"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "adaptative inference cost with convolutional neural mixture models"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "searching for accurate binary neural architectures"
    - "auxiliary learning for deep multi task learning"
    - "binary ensemble neural network more bits per network or more networks per bit"

Connected through hyperedge HE-2 (induced by source paper "scan a scalable neural networks framework towards compact and efficient models"):
  with members:
    - "slimmable neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "skipnet learning dynamic routing in convolutional networks"
    - "deep mutual learning"
    - "attention is all you need"
    - "blockdrop dynamic inference paths in residual networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"

Connected through hyperedge HE-3 (induced by source paper "preparing lessons improve knowledge distillation with better supervision"):
  with members:
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "highlight every step knowledge distillation via collaborative teaching"
    - "revisit knowledge distillation a teacher free framework"
    - "similarity preserving knowledge distillation"
    - "adaptive regularization of labels"
    - "cinic 10 is not imagenet or cifar 10"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "msd multi self distillation learning via multi classifiers within deep neural networks"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "skipnet learning dynamic routing in convolutional networks"
    - "deep mutual learning"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "multi scale dense networks for resource efficient image classification"
    - "data free knowledge distillation for deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

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)