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: "the vertica analytic database c store 7 years later"
  Abstract: This paper describes the system architecture of the Vertica Analytic Database (Vertica), a commercialization of the design of the C-Store research prototype. Vertica demonstrates a modern commercial RDBMS system that presents a classical relational interface while at the same time achieving the high performance expected from modern "web scale" analytic systems by making appropriate architectural choices. Vertica is also an instructive lesson in how academic systems research can be directly commercialized into a successful product.

Connected through hyperedge HE-1 (induced by source paper "big data architecture in czech republic healthcare service requirements tpc h benchmarks and vertica"):
  with members:
    - "towards the next generation of exergames flexible and personalised assessment based identification of tennis swings"
    - "computational intelligence for qualitative coaching diagnostics automated assessment of tennis swings to improve performance and safety"
    - "a hybrid ict solution for smart meter data analytics"

Connected through hyperedge HE-2 (induced by source paper "a computational model for analytic column stores"):
  with members:
    - "better bitmap performance with roaring bitmaps"
    - "processing a trillion cells per mouse click"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for join order enumeration"):
  with members:
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"
    - "database meets deep learning challenges and opportunities"
    - "asynchronous methods for deep reinforcement learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "version 0 1 of the bigdawg polystore system"):
  with members:
    - "sok cryptographically protected database search"
    - "the bigdawg polystore system and architecture"
    - "computing on masked data to improve the security of big data"

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)