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: "adaptive admm with spectral penalty parameter selection"
  Abstract: The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions. Unfortunately, its performance is highly sensitive to a penalty parameter, which makes ADMM often unreliable and hard to automate for a non-expert user. We tackle this weakness of ADMM by proposing a method to adaptively tune the penalty parameters to achieve fast convergence. The resulting adaptive ADMM (AADMM) algorithm, inspired by the successful Barzilai-Borwein spectral method for gradient descent, yields fast convergence and relative insensitivity to the initial stepsize and problem scaling.

Connected through hyperedge HE-1 (induced by source paper "catching cheats detecting strategic manipulation in distributed optimisation of electric vehicle aggregators"):
  with members:
    - "complexity of scheduling charging in the smart grid"
    - "optimization algorithms for catching data manipulators in power system estimation loops"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-2 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems"):
  with members:
    - "a unified 2d 3d large scale software environment for nonlinear inverse problems"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "image restoration using autoencoding priors"
    - "adaptive consensus admm for distributed optimization"
    - "devito automated fast finite difference computation"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "jinv a flexible julia package for pde parameter estimation"

Connected through hyperedge HE-3 (induced by source paper "recycled admm improving the privacy and accuracy of distributed algorithms"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "recycled admm improve privacy and accuracy with less computation in distributed algorithms"
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "adaptive consensus admm for distributed optimization"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "differentially private distributed optimization"

Connected through hyperedge HE-4 (induced by source paper "efficient solvers for sparse subspace clustering"):
  with members:
    - "approximating spectral clustering via sampling a review"
    - "large scale sparse subspace clustering using landmarks"
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "theoretical analysis of sparse subspace clustering with missing entries"
    - "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"
    - "unconstrained and curvature constrained shortest path distances and their approximation"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-5 (induced by source paper "newton admm a distributed gpu accelerated optimizer for multiclass classification problems"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "giant globally improved approximate newton method for distributed optimization"
    - "revisiting distributed synchronous sgd"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "adaptive consensus admm for distributed optimization"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "dynanewton accelerating newton s method for machine learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "recycled admm improve privacy and accuracy with less computation in distributed algorithms"):
  with members:
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "distributed constraint optimization problems and applications a survey"
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "adaptive consensus admm for distributed optimization"
    - "distributed event localization via alternating direction method of multipliers"
    - "differentially private distributed optimization"

Connected through hyperedge HE-7 (induced by source paper "improving the privacy and accuracy of admm based distributed algorithms"):
  with members:
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "differentially private distributed optimization"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "adaptive relaxed admm convergence theory and practical implementation"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "non negative factorization of the occurrence tensor from financial contracts"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "training neural networks without gradients a scalable admm approach"
    - "efficient training of very deep neural networks for supervised hashing"
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "unwrapping admm efficient distributed computing via transpose reduction"
    - "robust camera location estimation by convex programming"

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)