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: "net trim convex pruning of deep neural networks with performance guarantee"
  Abstract: We introduce and analyze a new technique for model reduction for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfitting and model redundancy negatively affects the prediction accuracy and model variance. Our Net-Trim algorithm prunes (sparsifies) a trained network layer-wise, removing connections at each layer by solving a convex optimization program. This program seeks a sparse set of weights at each layer that keeps the layer inputs and outputs consistent with the originally trained model. The algorithms and associated analysis are applicable to neural networks operating with the rectified linear unit (ReLU) as the nonlinear activation. We present both parallel and cascade versions of the algorithm. While the latter can achieve slightly simpler models with the same generalization performance, the former can be computed in a distributed manner. In both cases, Net-Trim significantly reduces the number of connections in the network, while also providing enough regularization to slightly reduce the generalization error. We also provide a mathematical analysis of the consistency between the initial network and the retrained model. To analyze the model sample complexity, we derive the general sufficient conditions for the recovery of a sparse transform matrix. For a single layer taking independent Gaussian random vectors of length $N$ as inputs, we show that if the network response can be described using a maximum number of $s$ non-zero weights per node, these weights can be learned from $\mathcal{O}(s\log N)$ samples.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "channel pruning via automatic structure search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "play and prune adaptive filter pruning for deep model compression"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-3 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-4 (induced by source paper "ternary mobilenets via per layer hybrid filter banks"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "searching for mobilenetv3"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-5 (induced by source paper "class dependent compression of deep neural networks"):
  with members:
    - "knowledge squeezed adversarial network compression"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "class balanced loss based on effective number of samples"
    - "darc differentiable architecture compression"
    - "compressing gans using knowledge distillation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "active subspace of neural networks structural analysis and universal attacks"):
  with members:
    - "universal adversarial audio perturbations"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "bayesian tensorized neural networks with automatic rank selection"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "gradient descent based d optimal design for the least squares polynomial approximation"

Connected through hyperedge HE-8 (induced by source paper "sipping neural networks sensitivity informed provable pruning of neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "on coresets for logistic regression"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "training support vector machines using coresets"

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)