human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stop that join discarding dimension tables when learning high capacity classifiers"):
  Abstract: Many datasets have multiple tables connected by key-foreign key dependencies. Data scientists usually join all tables to bring in extra features from the so-called dimension tables. Unlike the statistical relational learning setting, such joins do not cause record duplications, which means regular IID models are typically used. Recent work demonstrated the possibility of using foreign key features as representatives for the dimension tables' features and eliminating the latter a priori, potentially saving runtime and effort of data scientists. However, the prior work was restricted to linear models and it established a dichotomy of when dimension tables are safe to discard due to extra overfitting caused by the use of foreign key features. In this work, we revisit that question for two popular high capacity models: decision tree and SVM with RBF kernel. Our extensive empirical and simulation-based analyses show that these two classifiers are surprisingly and counter-intuitively more robust to discarding dimension tables and face much less extra overfitting than linear models. We provide intuitive explanations for their behavior and identify new open questions for further ML theoretical research. We also identify and resolve two key practical bottlenecks in using foreign key features.

Includes members:
  Member "a method for implementing a probabilistic model as a relational database":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "are key foreign key joins safe to avoid when learning high capacity classifiers")
  Member "feature hashing for large scale multitask learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")

Question: Please tell me which class the source paper 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)