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: "shareddb killing one thousand queries with one stone"
  Abstract: Traditional database systems are built around the query-at-a-time model. This approach tries to optimize performance in a best-effort way. Unfortunately, best effort is not good enough for many modern applications. These applications require response time guarantees in high load situations. This paper describes the design of a new database architecture that is based on batching queries and shared computation across possibly hundreds of concurrent queries and updates. Performance experiments with the TPC-W benchmark show that the performance of our implementation, SharedDB, is indeed robust across a wide range of dynamic workloads.

Connected through hyperedge HE-1 (induced by source paper "mothernets rapid deep ensemble learning"):
  with members:
    - "self training ensemble networks for zero shot image recognition"
    - "efficient architecture search by network transformation"
    - "snapshot ensembles train 1 get m for free"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "learning without forgetting"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "towards scalable dataframe systems"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "ray a distributed framework for emerging ai applications"
    - "adaptive sampling for rapidly matching histograms"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "towards a holistic integration of spreadsheets with databases a scalable storage engine for presentational data management"
    - "lara a key value algebra underlying arrays and relations"
    - "visualization aware sampling for very large databases"

Connected through hyperedge HE-3 (induced by source paper "cache based multi query optimization for data intensive scalable computing frameworks"):
  with members:
    - "revisiting reuse in main memory database systems"
    - "restore reusing results of mapreduce jobs"
    - "efficient and extensible algorithms for multi query optimization"

Connected through hyperedge HE-4 (induced by source paper "shared arrangements practical inter query sharing for streaming dataflows"):
  with members:
    - "scaling up in memory datalog processing observations and techniques"
    - "transaction repair full serializability without locks"

Connected through hyperedge HE-5 (induced by source paper "pay one get hundreds for free reducing cloud costs through shared query execution"):
  with members:
    - "efficient and extensible algorithms for multi query optimization"

Connected through hyperedge HE-6 (induced by source paper "database backed applications in the wild how well do they work"):
  with members:
    - "pipegen data pipe generator for hybrid analytics"
    - "program transformations for asynchronous and batched query submission"
    - "on predictive modeling for optimizing transaction execution in parallel oltp systems"

Connected through hyperedge HE-7 (induced by source paper "revisiting reuse in main memory database systems"):
  with members:
    - "forecasting the cost of processing multi join queries via hashing for main memory databases"

Connected through hyperedge HE-8 (induced by source paper "forecasting the cost of processing multi join queries via hashing for main memory databases"):
  with members:
    - "uncertainty aware query execution time prediction"
    - "robust estimation of resource consumption for sql queries using statistical techniques"

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)