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: "declarative machine learning a classification of basic properties and types"
  Abstract: Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. The fundamental goal is to simplify the usage and/or development of ML algorithms, which is especially important in the context of large-scale computations. However, ML systems at different abstraction levels have emerged over time and accordingly there has been a controversy about the meaning of this general definition of declarative ML. Specification alternatives range from ML algorithms expressed in domain-specific languages (DSLs) with optimization for performance, to ML task (learning problem) specifications with optimization for performance and accuracy. We argue that these different types of declarative ML complement each other as they address different users (data scientists and end users). This paper makes an attempt to create a taxonomy for declarative ML, including a definition of essential basic properties and types of declarative ML. Along the way, we provide insights into implications of these properties. We also use this taxonomy to classify existing systems. Finally, we draw conclusions on defining appropriate benchmarks and specification languages for declarative ML.

Connected through hyperedge HE-1 (induced by source paper "declarative data analytics a survey"):
  with members:
    - "optimization of imperative programs in a relational database"
    - "in database learning with sparse tensors"
    - "towards linear algebra over normalized data"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "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-2 (induced by source paper "helix holistic optimization for accelerating iterative machine learning"):
  with members:
    - "accelerating human in the loop machine learning challenges and opportunities"
    - "helix accelerating human in the loop machine learning"
    - "how developers iterate on machine learning workflows a survey of the applied machine learning literature"
    - "snorkel rapid training data creation with weak supervision"
    - "towards linear algebra over normalized data"
    - "activeclean interactive data cleaning while learning convex loss models"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "big data systems meet machine learning challenges towards big data science as a service"):
  with members:
    - "infrastructure for usable machine learning the stanford dawn project"
    - "f2 reenvisioning data analytics systems"
    - "modelhub towards unified data and lifecycle management for deep learning"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "mllib machine learning in apache spark"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "the madlib analytics library or mad skills the sql"
    - "distributed graphlab a framework for machine learning 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)