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: "learning sparse networks using targeted dropout"
  Abstract: Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning procedure that first learns a large net and then prunes away connections or hidden units. But standard training does not necessarily encourage nets to be amenable to pruning. We introduce targeted dropout, a method for training a neural network so that it is robust to subsequent pruning. Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights. The resulting network is robust to post hoc pruning of weights or units that frequently occur in the dropped sets. The method improves upon more complicated sparsifying regularisers while being simple to implement and easy to tune.

Connected through hyperedge HE-1 (induced by source paper "neural network compression framework for fast model inference"):
  with members:
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "cars continuous evolution for efficient neural architecture search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "progressive darts bridging the optimization gap for nas in the wild"
    - "autogan neural architecture search for generative adversarial networks"
    - "auto reid searching for a part aware convnet for person re identification"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "efficient residual dense block search for image super resolution"

Connected through hyperedge HE-3 (induced by source paper "discovering neural wirings"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "sparse networks from scratch faster training without losing performance"
    - "luck matters understanding training dynamics of deep relu networks"
    - "learning implicitly recurrent cnns through parameter sharing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning with chromatic networks"):
  with members:
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "policies modulating trajectory generators"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "playing atari with six neurons"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-5 (induced by source paper "grouped sparse projection"):
  with members:
    - "deephoyer learning sparser neural network with differentiable scale invariant sparsity measures"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "importance estimation for neural network pruning"
    - "the state of sparsity in deep neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "efficient dictionary learning with sparseness enforcing projections"

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)