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: "petascale computational systems"
  Abstract: Computational science is changing to be data intensive. Super-Computers must be balanced systems; not just CPU farms but also petascale IO and networking arrays. Anyone building CyberInfrastructure should allocate resources to support a balanced Tier-1 through Tier-3 design.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "designing for reproducibility a qualitative study of challenges and opportunities in high energy physics"):
  with members:
    - "the research object suite of ontologies sharing and exchanging research data and methods on the open web"
    - "citing and reading behaviours in high energy physics how a community stopped worrying about journals and learned to love repositories"

Connected through hyperedge HE-3 (induced by source paper "the family of mapreduce and large scale data processing systems"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "restore reusing results of mapreduce jobs"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "stubby a transformation based optimizer for mapreduce workflows"
    - "automatic optimization for mapreduce programs"
    - "fast clustering using mapreduce"
    - "load balancing for mapreduce based entity resolution"
    - "social content matching in mapreduce"

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.DB(Databases)