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: "a field guide to forward backward splitting with a fasta implementation"
  Abstract: Non-differentiable and constrained optimization play a key role in machine learning, signal and image processing, communications, and beyond. For high-dimensional minimization problems involving large datasets or many unknowns, the forward-backward splitting method provides a simple, practical solver. Despite its apparently simplicity, the performance of the forward-backward splitting is highly sensitive to implementation details. #R##N#This article is an introductory review of forward-backward splitting with a special emphasis on practical implementation concerns. Issues like stepsize selection, acceleration, stopping conditions, and initialization are considered. Numerical experiments are used to compare the effectiveness of different approaches. #R##N#Many variations of forward-backward splitting are implemented in the solver FASTA (short for Fast Adaptive Shrinkage/Thresholding Algorithm). FASTA provides a simple interface for applying forward-backward splitting to a broad range of problems.

Connected through hyperedge HE-1 (induced by source paper "towards compact convnets via structure sparsity regularized filter pruning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "attention based guided structured sparsity of deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "towards optimal structured cnn pruning via generative adversarial learning"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "discrimination aware channel pruning for deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-4 (induced by source paper "adversarial attacks on copyright detection systems"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "adversarial training for free"
    - "targeted adversarial examples for black box audio systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "universal adversarial training"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-5 (induced by source paper "neural network optimized 1 bit precoding for massive mu mimo"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "multilevel mimo detection with deep learning"
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "quantized constant envelope precoding with psk and qam signaling"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "1 bit massive mu mimo precoding in vlsi"

Connected through hyperedge HE-6 (induced by source paper "freelb enhanced adversarial training for language understanding"):
  with members:
    - "adversarial attacks on copyright detection systems"
    - "adversarial training for free"
    - "robust neural machine translation with doubly adversarial inputs"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-7 (induced by source paper "universal adversarial training"):
  with members:
    - "adversarial attacks on copyright detection systems"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "playing the game of universal adversarial perturbations"
    - "adversarial patch"
    - "ask acquire and attack data free uap generation using class impressions"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "defending against universal perturbations with shared adversarial training"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "forward stability of resnet and its variants"):
  with members:
    - "deep neural nets with interpolating function as output activation"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "mathematics of deep learning"
    - "deep residual learning and pdes on manifold"
    - "visualizing the loss landscape of neural nets"

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.NA(Numerical Analysis)