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: "unwrapping admm efficient distributed computing via transpose reduction"
  Abstract: Recent approaches to distributed model fitting rely heavily on consensus ADMM, where each node solves small sub-problems using only local data. We propose iterative methods that solve {\em global} sub-problems over an entire distributed dataset. This is possible using transpose reduction strategies that allow a single node to solve least-squares over massive datasets without putting all the data in one place. This results in simple iterative methods that avoid the expensive inner loops required for consensus methods. To demonstrate the efficiency of this approach, we fit linear classifiers and sparse linear models to datasets over 5 Tb in size using a distributed implementation with over 7000 cores in far less time than previous approaches.

Connected through hyperedge HE-1 (induced by source paper "distributed learning over unreliable networks"):
  with members:
    - "byzantine stochastic gradient descent"
    - "communication compression for decentralized training"
    - "sparcml high performance sparse communication for machine learning"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cola decentralized linear learning"

Connected through hyperedge HE-2 (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"
    - "fast admm algorithm for distributed optimization with adaptive penalty"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-3 (induced by source paper "training neural networks without gradients a scalable admm approach"):
  with members:
    - "deep learning with elastic averaging sgd"
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "accelerating hessian free optimization for deep neural networks by implicit preconditioning and sampling"
    - "distributed optimization of deeply nested systems"

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.DC(Distributed, Parallel, and Cluster Computing)