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: "optimizations and heuristics to improve compression in columnar database systems"
  Abstract: In-memory columnar databases have become mainstream over the last decade and have vastly improved the fast processing of large volumes of data through multi-core parallelism and in-memory compression thereby eliminating the usual bottlenecks associated with disk-based databases. For scenarios, where the data volume grows into terabytes and petabytes, keeping all the data in memory is exorbitantly expensive. Hence, the data is compressed efficiently using different algorithms to exploit the multi-core parallelization technologies for query processing. Several compression methods are studied for compressing the column array, post Dictionary Encoding. In this paper, we will present two novel optimizations in compression techniques - Block Size Optimized Cluster Encoding and Block Size Optimized Indirect Encoding - which perform better than their predecessors. In the end, we also propose heuristics to choose the best encoding amongst common compression schemes.

Connected through hyperedge HE-1 (induced by source paper "columnar database techniques for creating ai features"):
  with members:
    - "learning interesting categorical attributes for refined data exploration"
    - "wide deep learning for recommender systems"
    - "data compression for analytics over large scale in memory column databases"

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)