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: "e2 train training state of the art cnns with over 80 energy savings"
  Abstract: Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training. We strive to reduce the energy cost during training, by dropping unnecessary computations from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. For example, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring a top-1 accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving is achieved without degrading inference accuracy.

Connected through hyperedge HE-1 (induced by source paper "towards byzantine resilient learning in decentralized systems"):
  with members:
    - "zeno robust fully asynchronous sgd"
    - "slsgd secure and efficient distributed on device machine learning"
    - "towards federated learning at scale system design"
    - "zeno robust asynchronous sgd with arbitrary number of byzantine workers"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "a little is enough circumventing defenses for distributed learning"
    - "communication compression for decentralized training"
    - "generalized byzantine tolerant sgd"

Connected through hyperedge HE-2 (induced by source paper "autodnnchip an automated dnn chip predictor and builder for both fpgas and asics"):
  with members:
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "face recognition with hybrid efficient convolution algorithms on fpgas"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-3 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures"):
  with members:
    - "skynet a champion model for dac sdc on low power object detection"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "dnn dataflow choice is overrated"
    - "scale sim systolic cnn accelerator simulator"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "eie efficient inference engine on compressed deep neural network"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-4 (induced by source paper "ghostnet more features from cheap operations"):
  with members:
    - "learning instance wise sparsity for accelerating deep models"
    - "autogan neural architecture search for generative adversarial networks"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "fasterseg searching for faster real time semantic segmentation"
    - "searching for mobilenetv3"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"
    - "searching for accurate binary neural architectures"

Connected through hyperedge HE-5 (induced by source paper "drawing early bird tickets towards more efficient training of deep networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "swalp stochastic weight averaging in low precision training"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "energy and policy considerations for deep learning in nlp"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of 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.LG(Machine Learning)