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 a unified architecture for in rdbms analytics"
  Abstract: The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical technique must be implemented from scratch in the RDBMS, which leads to a lengthy and complex development process. We argue that the root cause for this overhead is the lack of a unified architecture for in-database analytics. Our main contribution in this work is to take a step towards such a unified architecture. A key benefit of our unified architecture is that performance optimizations for analytics techniques can be studied generically instead of an ad hoc, per-technique fashion. In particular, our technical contributions are theoretical and empirical studies of two key factors that we found impact performance: the order data is stored, and parallelization of computations on a single-node multicore RDBMS. We demonstrate the feasibility of our architecture by integrating several popular analytics techniques into two commercial and one open-source RDBMS. Our architecture requires changes to only a few dozen lines of code to integrate a new statistical technique. We then compare our approach with the native analytics tools offered by the commercial RDBMSes on various analytics tasks, and validate that our approach achieves competitive or higher performance, while still achieving the same quality.

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 "sqlflow a bridge between sql and machine learning"):
  with members:
    - "a unified approach to interpreting model predictions"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "physical representation based predicate optimization for a visual analytics database"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "noscope optimizing neural network queries over video at scale"

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 "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"

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)