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: "ambitious data science can be painless"
  Abstract: Modern data science research can involve massive computational experimentation; an ambitious PhD in computational fields may do experiments consuming several million CPU hours. Traditional computing practices, in which researchers use laptops or shared campus-resident resources, are inadequate for experiments at the massive scale and varied scope that we now see in data science. On the other hand, modern cloud computing promises seemingly unlimited computational resources that can be custom configured, and seems to offer a powerful new venue for ambitious data-driven science. Exploiting the cloud fully, the amount of work that could be completed in a fixed amount of time can expand by several orders of magnitude. #R##N#As potentially powerful as cloud-based experimentation may be in the abstract, it has not yet become a standard option for researchers in many academic disciplines. The prospect of actually conducting massive computational experiments in today's cloud systems confronts the potential user with daunting challenges. Leading considerations include: (i) the seeming complexity of today's cloud computing interface, (ii) the difficulty of executing an overwhelmingly large number of jobs, and (iii) the difficulty of monitoring and combining a massive collection of separate results. Starting a massive experiment `bare-handed' seems therefore highly problematic and prone to rapid `researcher burn out'. #R##N#New software stacks are emerging that render massive cloud experiments relatively painless. Such stacks simplify experimentation by systematizing experiment definition, automating distribution and management of tasks, and allowing easy harvesting of results and documentation. In this article, we discuss several painless computing stacks that abstract away the difficulties of massive experimentation, thereby allowing a proliferation of ambitious experiments for scientific discovery.

Connected through hyperedge HE-1 (induced by source paper "the full spectrum of deepnet hessians at scale dynamics with sgd training and sample size"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "exploring the limits of weakly supervised pretraining"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"
    - "eigenvalues of the hessian in deep learning singularity and beyond"

Connected through hyperedge HE-2 (induced by source paper "the full spectrum of deep net hessians at scale dynamics with sample size"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"

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)