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: "fdb a query engine for factorised relational databases"
  Abstract: Factorised databases are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. This paper introduces FDB, an in-memory query engine for select-project-join queries on factorised databases. Key components of FDB are novel algorithms for query optimisation and evaluation that exploit the succinctness brought by data factorisation. Experiments show that for data sets with many-to-many relationships FDB can outperform relational engines by orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "providing insights for queries affected by failures and stragglers"):
  with members:
    - "shark sql and rich analytics at scale"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-2 (induced by source paper "tuple oriented compression for large scale mini batch stochastic gradient descent"):
  with members:
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "a cost based optimizer for gradient descent optimization"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "towards linear algebra over normalized data"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-3 (induced by source paper "a layered aggregate engine for analytics workloads"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "in database learning with sparse tensors"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "ranked enumeration of conjunctive query results"):
  with members:
    - "any k anytime top k tree pattern retrieval in labeled graphs"
    - "answering fo mod queries under updates on bounded degree databases"
    - "size bounds and query plans for relational joins"
    - "answering conjunctive queries under updates"
    - "compressed representations of conjunctive query results"
    - "skew strikes back new developments in the theory of join algorithms"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-5 (induced by source paper "compressed representations of conjunctive query results"):
  with members:
    - "size bounds and query plans for relational joins"
    - "conditional lower bounds for space time tradeoffs"
    - "answering conjunctive queries under updates"
    - "extracting and analyzing hidden graphs from relational databases"
    - "data structure lower bounds for document indexing problems"
    - "aggregation and ordering in factorised databases"
    - "structural tractability of counting of solutions to conjunctive queries"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-6 (induced by source paper "when lempel ziv welch meets machine learning a case study of accelerating machine learning using coding"):
  with members:
    - "compressing neural networks with the hashing trick"
    - "aggregation and ordering in factorised databases"
    - "scalable k means"

Connected through hyperedge HE-7 (induced by source paper "towards linear algebra over normalized data"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "linview incremental view maintenance for complex analytical queries"
    - "aggregation and ordering in factorised databases"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"

Connected through hyperedge HE-8 (induced by source paper "join size estimation on boolean tensors of rdf data"):
  with members:
    - "a technique for sparql optional left outer join queries"
    - "better size estimation for sparse matrix products"

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)