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: "optimization with sparsity inducing penalties"
  Abstract: Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. They were first dedicated to linear variable selection but numerous extensions have now emerged such as structured sparsity or kernel selection. It turns out that many of the related estimation problems can be cast as convex optimization problems by regularizing the empirical risk with appropriate non-smooth norms. The goal of this paper is to present from a general perspective optimization tools and techniques dedicated to such sparsity-inducing penalties. We cover proximal methods, block-coordinate descent, reweighted $\ell_2$-penalized techniques, working-set and homotopy methods, as well as non-convex formulations and extensions, and provide an extensive set of experiments to compare various algorithms from a computational point of view.

Connected through hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "exploiting computation replication for mobile edge computing a fundamental computation communication tradeoff study"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "energy efficient processing and robust wireless cooperative transmission for edge inference"
    - "the roadmap to 6g ai empowered wireless networks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "data shuffling in wireless distributed computing via low rank optimization"

Connected through hyperedge HE-2 (induced by source paper "learning with differentiable perturbed optimizers"):
  with members:
    - "differentiable convex optimization layers"
    - "differentiation of blackbox combinatorial solvers"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "online linear optimization via smoothing"
    - "on measure concentration of random maximum a posteriori perturbations"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-3 (induced by source paper "on the effectiveness of richardson extrapolation in machine learning"):
  with members:
    - "breaking the curse of dimensionality with convex neural networks"
    - "super samples from kernel herding"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-4 (induced by source paper "sparse and structured visual attention"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "sparse sequence to sequence models"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "neural baby talk"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "sparse and constrained attention for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"

Connected through hyperedge HE-6 (induced by source paper "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "polysemous codes"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"

Connected through hyperedge HE-7 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"

Connected through hyperedge HE-8 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "reliable federated learning for mobile networks"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "limited feedback in single and multi user mimo systems with finite bit adcs"

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)