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: "towards linear algebra over normalized data"
  Abstract: Providing machine learning (ML) over relational data is a mainstream requirement for data analytics systems. While almost all the ML tools require the input data to be presented as a single table, many datasets are multi-table, which forces data scientists to join those tables first, leading to data redundancy and runtime waste. Recent works on factorized ML mitigate this issue for a few specific ML algorithms by pushing ML through joins. But their approaches require a manual rewrite of ML implementations. Such piecemeal methods create a massive development overhead when extending such ideas to other ML algorithms. In this paper, we show that it is possible to mitigate this overhead by leveraging a popular formal algebra to represent the computations of many ML algorithms: linear algebra. We introduce a new logical data type to represent normalized data and devise a framework of algebraic rewrite rules to convert a large set of linear algebra operations over denormalized data into operations over the normalized data. We show how this enables us to automatically factorize several popular ML algorithms, thus unifying and generalizing several prior works. We prototype our framework in the popular ML environment R and an industrial R-over-RDBMS tool. Experiments with both synthetic and real normalized data show that our framework also yields significant speed-ups, up to $26$x on real data.

Connected through hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "ac dc in database learning thunderstruck"
    - "efficient differentiable programming in a functional array processing language"
    - "stream fusion to completeness"
    - "building efficient query engines in a high level language"
    - "xgboost a scalable tree boosting system"
    - "hpat high performance analytics with scripting ease of use"
    - "tensorflow a system for large scale machine learning"

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"
    - "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"
    - "compressing neural networks with the hashing trick"

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"
    - "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-5 (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"
    - "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-6 (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"
    - "in database learning with sparse tensors"
    - "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-7 (induced by source paper "a survey on data collection for machine learning a big data ai integration perspective"):
  with members:
    - "data cleaning for accurate fair and robust models a big data ai integration approach"
    - "the gan landscape losses architectures regularization and normalization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "data synthesis based on generative adversarial networks"
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "a survey on deep transfer learning"
    - "learning based video motion magnification"

Connected through hyperedge HE-8 (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"
    - "activeclean interactive data cleaning while learning convex loss models"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "declarative machine learning a classification of basic properties and types"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)