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: "optimization of imperative programs in a relational database"
  Abstract: For decades, RDBMSs have supported declarative SQL as well as imperative functions and procedures as ways for users to express data processing tasks. While the evaluation of declarative SQL has received a lot of attention resulting in highly sophisticated techniques, the evaluation of imperative programs has remained naive and highly inefficient. Imperative programs offer several benefits over SQL and hence are often preferred and widely used. But unfortunately, their abysmal performance discourages, and even prohibits their use in many situations. We address this important problem that has hitherto received little attention. #R##N#We present Froid, an extensible framework for optimizing imperative programs in relational databases. Froid's novel approach automatically transforms entire User Defined Functions (UDFs) into relational algebraic expressions, and embeds them into the calling SQL query. This form is now amenable to cost-based optimization and results in efficient, set-oriented, parallel plans as opposed to inefficient, iterative, serial execution of UDFs. Froid's approach additionally brings the benefits of many compiler optimizations to UDFs with no additional implementation effort. We describe the design of Froid and present our experimental evaluation that demonstrates performance improvements of up to multiple orders of magnitude on real workloads.

Connected through hyperedge HE-1 (induced by source paper "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"):
  with members:
    - "extending relational query processing with ml inference"
    - "machine learning at microsoft with ml net"
    - "data science through the looking glass and what we found there"
    - "query and resource optimizations a case for breaking the wall in big data systems"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "neural ordinary differential equations"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-2 (induced by source paper "extending relational query processing with ml inference"):
  with members:
    - "willump a statistically aware end to end optimizer for machine learning inference"
    - "machine learning at microsoft with ml net"
    - "cloudy with high chance of dbms a 10 year prediction for enterprise grade ml"
    - "declarative recursive computation on an rdbms or why you should use a database for distributed machine learning"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "automatically leveraging mapreduce frameworks for data intensive applications"
    - "laradb a minimalist kernel for linear and relational algebra computation"

Connected through hyperedge HE-3 (induced by source paper "declarative data analytics a survey"):
  with members:
    - "in database learning with sparse tensors"
    - "towards linear algebra over normalized data"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "declarative machine learning a classification of basic properties and types"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "automatic differentiation in machine learning a survey"
    - "efficient iterative processing in the scidb parallel array engine"

Connected through hyperedge HE-4 (induced by source paper "reactors a case for predictable virtualized actor database systems"):
  with members:
    - "transactional partitioning a new abstraction for main memory databases"
    - "design principles for scaling multi core oltp under high contention"
    - "warp lightweight multi key transactions for key value stores"
    - "oltp on hardware islands"
    - "elastras an elastic transactional data store in the cloud"

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)