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: "multi label classification methods for multi target regression"
  Abstract: Real world prediction problems often involve the simultaneous prediction of multiple target variables using the same set of predictive variables. When the target variables are binary, the prediction task is called multi-label classification while when the target variables are real-valued the task is called multi-target regression. Although multi-target regression attracted the attention of the research community prior to multi-label classification, the recent advances in this field motivate a study of whether newer state-of-the-art algorithms developed for multi-label classification are applicable and equally successful in the domain of multi-target regression. In this paper we introduce two new multi-target regression algorithms: multi-target stacking (MTS) and ensemble of regressor chains (ERC), inspired by two popular multi-label classification approaches that are based on a single-target decomposition of the multi-target problem and the idea of treating the other prediction targets as additional input variables that augment the input space. Furthermore, we detect an important shortcoming on both methods related to the methodology used to create the additional input variables and develop modified versions of the algorithms (MTSC and ERCC) to tackle it. All methods are empirically evaluated on 12 real-world multi-target regression datasets, 8 of which are first introduced in this paper and are made publicly available for future benchmarks. The experimental results show that ERCC performs significantly better than both a strong baseline that learns a single model for each target using bagging of regression trees and the state-of-the-art multi-objective random forest approach. Also, the proposed modification results in significant performance gains for both MTS and ERC.

Connected through hyperedge HE-1 (induced by source paper "a snapshot on nonstandard supervised learning problems taxonomy relationships problem transformations and algorithm adaptations"):
  with members:
    - "dealing with difficult minority labels in imbalanced mutilabel data sets"
    - "label distribution learning"
    - "multi instance multi label learning"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-2 (induced by source paper "multi target regression via random linear target combinations"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "multi target regression via input space expansion treating targets as inputs"
    - "maximum margin output coding"
    - "multi label prediction via compressed sensing"
    - "top down induction of clustering trees"

Connected through hyperedge HE-3 (induced by source paper "multi target regression via input space expansion treating targets as inputs"):
  with members:
    - "learning internal representations"
    - "rectifying classifier chains for multi label classification"
    - "multi target regression via random linear target combinations"
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"
    - "the landmark selection method for multiple output prediction"
    - "maximum margin output coding"
    - "a dirty model for multiple sparse regression"
    - "multi label prediction via compressed sensing"

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)