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: "in database learning with sparse tensors"
  Abstract: In-database analytics is of great practical importance as it avoids the costly repeated loop data scientists have to deal with on a daily basis: select features, export the data, convert data format, train models using an external tool, reimport the parameters. It is also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. #R##N#This paper introduces a unified framework for training and evaluating a class of statistical learning models inside a relational database. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from relational database theory such as schema information, query structure, recent advances in query evaluation algorithms, and from linear algebra such as various tensor and matrix operations, one can formulate in-database learning problems and design efficient algorithms to solve them. #R##N#The algorithms and models proposed in the paper have already been implemented and deployed in retail-planning and forecasting applications, with significant performance benefits over out-of-database solutions that require the costly data-export loop.

Connected through hyperedge HE-1 (induced by source paper "spores sum product optimization via relational equality saturation for large scale linear algebra"):
  with members:
    - "using e graphs for cad parameter inference"
    - "on optimizing operator fusion plans for large scale machine learning in systemml"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "solving the join ordering problem via mixed integer linear programming"
    - "faq questions asked frequently"
    - "equality saturation a new approach to optimization"

Connected through hyperedge HE-2 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "on functional aggregate queries with additive inequalities"
    - "coresets for k means and k median clustering and their applications"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "clustering high dimensional dynamic data streams"
    - "juggling functions inside a database"

Connected through hyperedge HE-3 (induced by source paper "a layered aggregate engine for analytics workloads"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "building efficient query engines in a high level language"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"
    - "the ctu prague relational learning repository"
    - "mllib machine learning in apache spark"
    - "faq questions asked frequently"

Connected through hyperedge HE-4 (induced by source paper "declarative data analytics a survey"):
  with members:
    - "optimization of imperative programs in a relational database"
    - "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-5 (induced by source paper "sql4ml a declarative end to end workflow for machine learning"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "declarative recursive computation on an rdbms or why you should use a database for distributed machine learning"
    - "on matrices and k relations"
    - "ac dc in database learning thunderstruck"
    - "towards linear algebra over normalized data"
    - "tensorflow a system for large scale machine learning"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"

Connected through hyperedge HE-6 (induced by source paper "bigdata applications from graph analytics to machine learning by aggregates in recursion"):
  with members:
    - "a case for stale synchronous distributed model for declarative recursive computation"
    - "scaling up reasoning and advanced analytics on bigdata"
    - "towards a unified architecture for in rdbms analytics"

Connected through hyperedge HE-7 (induced by source paper "worst case optimal join algorithms techniques results and open problems"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "juggling functions inside a database"
    - "worst case optimal algorithms for parallel query processing"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"

Connected through hyperedge HE-8 (induced by source paper "on functional aggregate queries with additive inequalities"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "worst case optimal join algorithms techniques results and open problems"
    - "learning models over relational data using sparse tensors and functional dependencies"
    - "constraint solving via fractional edge covers"
    - "juggling functions inside a database"
    - "boolean tensor decomposition for conjunctive queries with negation"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "faq questions asked frequently"

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)