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: "big data spark solution for functional magnetic resonance imaging"
  Abstract: Recently, Big Data applications have rapidly expanded into different industries. Healthcare is also one the industries willing to use big data platforms so that some big data analytics tools have been adopted in this field to some extent. Medical imaging which is a pillar in diagnostic healthcare deals with high volume of data collection and processing. A huge amount of 3D and 4D images are acquired in different forms and resolutions using a variety of medical imaging modalities. Preprocessing and analyzing imaging data is currently a long process and cost and time consuming. However, not many big data platforms have been provided or redesigned for medical imaging purposes because of some restrictions such as data format. In this paper, we designed, developed and successfully tested a new pipeline for medical imaging data (especially functional magnetic resonance imaging - fMRI) using Big Data Spark / PySpark platform on a single node which allows us to read and load imaging data, convert them to Resilient Distributed Datasets in order manipulate and perform in-memory data processing in parallel and convert final results to imaging format while the pipeline provides an option to store the results in other formats such as data frame. Using this new solution and pipeline, we repeated our previous works in which we extracted brain networks from fMRI data using template matching and sum of squared differences (SSD) method. The final results revealed our Spark (PySpark) based solution improved the performance (in terms of processing time) around 4 times on a single compared to the previous work developed in Python.

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"
    - "execution templates caching control plane decisions for strong scaling of data analytics"
    - "a survey on deep learning in medical image analysis"
    - "improved techniques for training gans"
    - "scalable fast cloud computing with execution templates"
    - "faster and more accurate sequence alignment with snap"
    - "petascale computational 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)