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: "aggregation and ordering in factorised databases"
  Abstract: A common approach to data analysis involves understanding and manipulating succinct representations of data. In earlier work, we put forward a succinct representation system for relational data called factorised databases and reported on the main-memory query engine FDB for select-project-join queries on such databases. #R##N#In this paper, we extend FDB to support a larger class of practical queries with aggregates and ordering. This requires novel optimisation and evaluation techniques. We show how factorisation coupled with partial aggregation can effectively reduce the number of operations needed for query evaluation. We also show how factorisations of query results can support enumeration of tuples in desired orders as efficiently as listing them from the unfactorised, sorted results. #R##N#We experimentally observe that FDB can outperform off-the-shelf relational engines by orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "myhill nerode methods for hypergraphs"

Connected through hyperedge HE-2 (induced by source paper "learning models over relational data a brief tutorial"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "incremental techniques for large scale dynamic query processing"
    - "counting triangles under updates in worst case optimal time"
    - "on functional aggregate queries with additive inequalities"
    - "size bounds and query plans for relational joins"
    - "incremental view maintenance with triple lock factorization benefits"
    - "constraint solving via fractional edge covers"
    - "boolean tensor decomposition for conjunctive queries with negation"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "topology dependent bounds for faqs"):
  with members:
    - "query to communication lifting for bpp"
    - "juggling functions inside a database"
    - "worst case optimal algorithms for parallel query processing"
    - "tight network topology dependent bounds on rounds of communication"
    - "the range of topological effects on communication"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "faq questions asked frequently"
    - "skew in parallel query processing"

Connected through hyperedge HE-6 (induced by source paper "conjunctive queries with theta joins under updates"):
  with members:
    - "incremental view maintenance with triple lock factorization benefits"
    - "answering conjunctive queries under updates"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-7 (induced by source paper "hyperbench a benchmark and tool for hypergraphs and empirical findings"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "old techniques for new join algorithms a case study in rdf processing"
    - "xcsp3 an integrated format for benchmarking combinatorial constrained problems"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"

Connected through hyperedge HE-8 (induced by source paper "ac dc in database learning thunderstruck"):
  with members:
    - "in database learning with sparse tensors"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"

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)