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: "the madlib analytics library or mad skills the sql"
  Abstract: MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to eventually serve a role for scalable database systems that is similar to the CRAN library for R: a community repository of statistical methods, this time written with scale and parallelism in mind. In this paper we introduce the MADlib project, including the background that led to its beginnings, and the motivation for its open source nature. We provide an overview of the library's architecture and design patterns, and provide a description of various statistical methods in that context. We include performance and speedup results of a core design pattern from one of those methods over the Greenplum parallel DBMS on a modest-sized test cluster. We then report on two initial efforts at incorporating academic research into MADlib, which is one of the project's goals. MADlib is freely available at this http URL, and the project is open for contributions of both new methods, and ports to additional database platforms.

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"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "hpat high performance analytics with scripting ease of use"

Connected through hyperedge HE-2 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (induced by source paper "accelerating generalized linear models with mlweaving a one size fits all system for any precision learning technical report"):
  with members:
    - "high accuracy low precision training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "in rdbms hardware acceleration of advanced analytics"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-4 (induced by source paper "systemds a declarative machine learning system for the end to end data science lifecycle"):
  with members:
    - "sato contextual semantic type detection in tables"
    - "holodetect few shot learning for error detection"
    - "sherlock a deep learning approach to semantic data type detection"
    - "towards federated learning at scale system design"
    - "autograph imperative style coding with graph based performance"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "autoaugment learning augmentation policies from data"
    - "helix holistic optimization for accelerating iterative machine learning"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "retro relation retrofitting for in database machine learning on textual data"):
  with members:
    - "termite a system for tunneling through heterogeneous data"
    - "idel in database entity linking with neural embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "enriching word vectors with subword information"
    - "counter fitting word vectors to linguistic constraints"
    - "an ensemble method to produce high quality word embeddings"

Connected through hyperedge HE-8 (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"

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)