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: "deep model compression distilling knowledge from noisy teachers"
  Abstract: The remarkable successes of deep learning models#R##N#across various applications have resulted in the design of#R##N#deeper networks that can solve complex problems. How-#R##N#ever, the increasing depth of such models also results in#R##N#a higher storage and runtime complexity, which restricts#R##N#the deployability of such very deep models on mobile and#R##N#portable devices, which have limited storage and battery#R##N#capacity. While many methods have been proposed for deep#R##N#model compression in recent years, almost all of them have#R##N#focused on reducing storage complexity. In this work, we#R##N#extend the teacher-student framework for deep model com-#R##N#pression, since it has the potential to address runtime and#R##N#train time complexity too. We propose a simple method-#R##N#ology to include a noise-based regularizer while training#R##N#the student from the teacher, which provides a healthy im-#R##N#provement in the performance of the student network. Our#R##N#experiments on the CIFAR-10, SVHN and MNIST datasets#R##N#show promising improvement, with the best performance on#R##N#the CIFAR-10 dataset. We also conduct a comprehensive#R##N#empirical evaluation of the proposed method under related#R##N#settings on the CIFAR-10 dataset to show the promise of the#R##N#proposed approach.

Connected through hyperedge HE-1 (induced by source paper "power bert accelerating bert inference for classification tasks"):
  with members:
    - "are sixteen heads really better than one"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "adaptively sparse transformers"
    - "q bert hessian based ultra low precision quantization of bert"
    - "sparse sequence to sequence models"
    - "explicit sparse transformer concentrated attention through explicit selection"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-2 (induced by source paper "margindistillation distillation for margin based softmax"):
  with members:
    - "triplet distillation for deep face recognition"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "relational knowledge distillation"
    - "learning student networks via feature embedding"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "sphereface deep hypersphere embedding for face recognition"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-3 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "amalgamating knowledge towards comprehensive classification"
    - "improving fast segmentation with teacher student learning"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "patient knowledge distillation for bert model compression"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "all you need is a few shifts designing efficient convolutional neural networks for image classification"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a layer decomposition recomposition framework for neuron pruning towards accurate lightweight networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "constructing fast network through deconstruction of convolution"
    - "darts differentiable architecture search"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "light multi segment activation for model compression"):
  with members:
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "wrpn wide reduced precision networks"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ternary neural networks with fine grained quantization"

Connected through hyperedge HE-7 (induced by source paper "correlation congruence for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil of face recognition is in the noise"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "arcface additive angular margin loss for deep face recognition"
    - "large scale distributed neural network training through online distillation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep mutual learning"

Connected through hyperedge HE-8 (induced by source paper "openei an open framework for edge intelligence"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "yolov3 an incremental improvement"
    - "in datacenter performance analysis of a tensor processing unit"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"

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)