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: "laradb a minimalist kernel for linear and relational algebra computation"
  Abstract: Analytics tasks manipulate structured data with variants of relational algebra (RA) and quantitative data with variants of linear algebra (LA). The two computational models have overlapping expressiveness, motivating a common programming model that affords unified reasoning and algorithm design. At the logical level we propose LARA, a lean algebra of three operators, that expresses RA and LA as well as relevant optimization rules. We show a series of proofs that position LARA at just the right level of expressiveness for a middleware algebra: more explicit than MapReduce but more general than RA or LA. At the physical level we find that the LARA operators afford efficient implementations using a single primitive that is available in a variety of backend engines: range scans over partitioned sorted maps.   To evaluate these ideas, we implemented the LARA operators as range iterators in Apache Accumulo, a popular implementation of Google's BigTable. First we show how LARA expresses a sensor quality control task, and we measure the performance impact of optimizations LARA admits on this task. Second we show that the LARADB implementation outperforms Accumulo's native MapReduce integration on a core task involving join and aggregation in the form of matrix multiply, especially at smaller scales that are typically a poor fit for scale-out approaches. We find that LARADB offers a conceptually lean framework for optimizing mixed-abstraction analytics tasks, without giving up fast record-level updates and scans.

Connected through hyperedge HE-1 (induced by source paper "towards scalable dataframe systems"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "ray a distributed framework for emerging ai applications"
    - "adaptive sampling for rapidly matching histograms"
    - "towards a holistic integration of spreadsheets with databases a scalable storage engine for presentational data management"
    - "lara a key value algebra underlying arrays and relations"
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"

Connected through hyperedge HE-2 (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"
    - "in database learning with sparse tensors"
    - "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-3 (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-4 (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"
    - "optimization of imperative programs in a relational database"

Connected through hyperedge HE-5 (induced by source paper "on matrices and k relations"):
  with members:
    - "on the expressive power of linear algebra on graphs"
    - "on the expressive power of query languages for matrices"
    - "polystore mathematics of relational algebra"
    - "juggling functions inside a database"
    - "faq questions asked frequently"

Connected through hyperedge HE-6 (induced by source paper "on the expressiveness of lara a unified language for linear and relational algebra"):
  with members:
    - "on the expressive power of linear algebra on graphs"
    - "research directions for principles of data management dagstuhl perspectives workshop 16151"
    - "on the expressive power of query languages for matrices"

Connected through hyperedge HE-7 (induced by source paper "ektelo a framework for defining differentially private computations"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "private selection from private candidates"
    - "synthesizing coupling proofs of differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "differentially private bayesian programming"
    - "principled evaluation of differentially private algorithms using dpbench"

Connected through hyperedge HE-8 (induced by source paper "on the expressive power of linear algebra on graphs"):
  with members:
    - "on the expressiveness of lara a unified language for linear and relational algebra"
    - "on the expressive power of query languages for matrices"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "towards linear algebra over normalized data"
    - "descriptive complexity of graph spectra"
    - "foundations of modern query languages for graph databases"
    - "rank logic is dead long live rank logic"
    - "dimension reduction via colour refinement"

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)