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: "oversketched newton fast convex optimization for serverless systems"
  Abstract: Motivated by recent developments in serverless systems for large-scale machine learning as well as improvements in scalable randomized matrix algorithms, we develop OverSketched Newton, a randomized Hessian-based optimization algorithm to solve large-scale smooth and strongly-convex problems in serverless systems. OverSketched Newton leverages matrix sketching ideas from Randomized Numerical Linear Algebra to compute the Hessian approximately. These sketching methods lead to inbuilt resiliency against stragglers that are a characteristic of serverless architectures. We establish that OverSketched Newton has a linear-quadratic convergence rate, and we empirically validate our results by solving large-scale supervised learning problems on real-world datasets. Experiments demonstrate a reduction of ~50% in total running time on AWS Lambda, compared to state-of-the-art distributed optimization schemes.

Connected through hyperedge HE-1 (induced by source paper "serverless straggler mitigation using local error correcting codes"):
  with members:
    - "polar coded distributed matrix multiplication"
    - "cloud programming simplified a berkeley view on serverless computing"
    - "collaborative decoding of polynomial codes for distributed computation"
    - "serverless computing one step forward two steps back"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "large batch size training of neural networks with adversarial training and second order information"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pyhessian neural networks through the lens of the hessian"
    - "inefficiency of k fac for large batch size training"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "image classification at supercomputer scale"
    - "hessian based analysis of large batch training and robustness to adversaries"

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)