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: "fast admm algorithm for distributed optimization with adaptive penalty"
  Abstract: We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including a computer vision application of distributed structure from motion.

Connected through hyperedge HE-1 (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"
    - "adaptive admm with spectral penalty parameter selection"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-2 (induced by source paper "a distributed admm approach to non myopic path planning for multi target tracking"):
  with members:
    - "potential game based non myopic sensor network planning for multi target tracking"
    - "a message passing algorithm for multi agent trajectory planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (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"
    - "adaptive admm with spectral penalty parameter selection"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "differentially private distributed optimization"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "accelerated methods for the socp relaxed component based distributed optimal power flow"):
  with members:
    - "recover feasible solutions for socp relaxation of optimal power flow problems in mesh networks"

Connected through hyperedge HE-5 (induced by source paper "iterative receivers for downlink mimo scma message passing and distributed cooperative detection"):
  with members:
    - "wireless powered communications with non orthogonal multiple access"
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "a survey of downlink non orthogonal multiple access for 5g wireless communication networks"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "scma codebook design"
    - "what will 5g be"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-6 (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"
    - "adaptive admm with spectral penalty parameter selection"
    - "training neural networks without gradients a scalable admm approach"
    - "efficient training of very deep neural networks for supervised hashing"
    - "unwrapping admm efficient distributed computing via transpose reduction"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-7 (induced by source paper "adaptive consensus admm for distributed optimization"):
  with members:
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "adaptive admm with spectral penalty parameter selection"
    - "training neural networks without gradients a scalable admm approach"

Connected through hyperedge HE-8 (induced by source paper "adaptive admm with spectral penalty parameter selection"):
  with members:
    - "a field guide to forward backward splitting with a fasta implementation"

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)