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: "execution templates caching control plane decisions for strong scaling of data analytics"
  Abstract: Control planes of cloud frameworks trade off between scheduling granularity and performance. Centralized systems schedule at task granularity, but only schedule a few thousand tasks per second. Distributed systems schedule hundreds of thousands of tasks per second but changing the schedule is costly. #R##N#We present execution templates, a control plane abstraction that can schedule hundreds of thousands of tasks per second while supporting fine-grained, per-task scheduling decisions. Execution templates leverage a program's repetitive control flow to cache blocks of frequently-executed tasks. Executing a task in a template requires sending a single message. Large-scale scheduling changes install new templates, while small changes apply edits to existing templates. #R##N#Evaluations of execution templates in Nimbus, a data analytics framework, find that they provide the fine-grained scheduling flexibility of centralized control planes while matching the strong scaling of distributed ones. Execution templates support complex, real-world applications, such as a fluid simulation with a triply nested loop and data dependent branches.

Connected through hyperedge HE-1 (induced by source paper "extending tensorflow s semantics with pipelined execution"):
  with members:
    - "dynamic control flow in large scale machine learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "a survey on deep learning in medical image analysis"
    - "big data spark solution for functional magnetic resonance imaging"
    - "improved techniques for training gans"
    - "scalable fast cloud computing with execution templates"
    - "faster and more accurate sequence alignment with snap"
    - "petascale computational systems"

Connected through hyperedge HE-2 (induced by source paper "the coming age of pervasive data processing"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "thrill high performance algorithmic distributed batch data processing with c"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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)