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: "robust logitboost and adaptive base class abc logitboost"
  Abstract: Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then propose abc-logitboost for multi-class classification, by combining robust logitboost with the prior work of abc-boost. Previously, abc-boost was implemented as abc-mart using the mart algorithm. Our extensive experiments on multi-class classification compare four algorithms: mart, abcmart, (robust) logitboost, and abc-logitboost, and demonstrate the superiority of abc-logitboost. Comparisons with other learning methods including SVM and deep learning are also available through prior publications.

Connected through hyperedge HE-1 (induced by source paper "an experimental evaluation of large scale gbdt systems"):
  with members:
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "deep forest towards an alternative to deep neural networks"
    - "optimal quantile approximation in streams"
    - "xgboost a scalable tree boosting system"
    - "dimmwitted a study of main memory statistical analytics"

Connected through hyperedge HE-2 (induced by source paper "rade resource efficient supervised anomaly detection using decision tree based ensemble methods"):
  with members:
    - "scarff a scalable framework for streaming credit card fraud detection with spark"
    - "forest packing fast parallel decision forests"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "gbdt mo gradient boosted decision trees for multiple outputs"):
  with members:
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "formal verification of decision tree ensemble model and detection of its violating input value ranges"):
  with members:
    - "a survey of symbolic execution techniques"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (induced by source paper "analysis of regression tree fitting algorithms in learning to rank"):
  with members:
    - "fast abc boost for multi class classification"
    - "direct optimization of ranking measures"

Connected through hyperedge HE-6 (induced by source paper "plackett luce model for learning to rank task"):
  with members:
    - "learning to order things"

Connected through hyperedge HE-7 (induced by source paper "second language acquisition modeling an ensemble approach"):
  with members:
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "linearized gmm kernels and normalized random fourier features"):
  with members:
    - "object proposal with kernelized partial ranking"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "on the error of random fourier features"
    - "scalable kernel methods via doubly stochastic gradients"
    - "sign stable projections sign cauchy projections and chi square kernels"

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)