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: "faster boosting with smaller memory"
  Abstract: The two state-of-the-art implementations of boosted trees: XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing boosted trees. which achieves a significant speedup over XGBoost and LightGBM, especially when memory size is small. This is achieved using a combination of two techniques: early stopping and stratified sampling, which are explained and analyzed in the paper. We describe our implementation and present experimental results to support our claims.

Connected through hyperedge HE-1 (induced by source paper "litemort a memory efficient gradient boosting tree system on adaptive compact distributions"):
  with members:
    - "gradient boosted feature selection"
    - "an experimental evaluation of large scale gbdt systems"
    - "catboost gradient boosting with categorical features support"
    - "xgboost a scalable tree boosting system"

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.LG(Machine Learning)