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 "multithreshold entropy linear classifier"):
  Abstract: We propose a new entropy based multithreshold linear classifier with an adaptive kernel density estimation.Proposed classifier maximizes multiple margins, while being conceptually similar in nature to SVM.This method gives good classification results and is especially designed for unbalanced datasets.It achieves significantly better results than SVM as part of an expert system designed for drug discovery.Resulting model provides insight into the internal data geometry and can detect multiple clusters. This paper proposes a new multithreshold linear classifier (MELC) based on the Renyi's quadratic entropy and Cauchy-Schwarz divergence, combined with the adaptive kernel density estimation in the one dimensional projections space. Due to its nature MELC is especially well adapted to deal with unbalanced data. As the consequence of both used model and the applied density regularization technique, it shows strong regularization properties and therefore is almost unable to overfit. Moreover, contrary to SVM, in its basic form it has no free parameters, however, at the cost of being a non-convex optimization problem which results in the existence of local optima and the possible need for multiple initializations.In practice, MELC obtained similar or higher scores than the ones given by SVM on both synthetic and real data from the UCI repository. We also perform experimental evaluation of proposed method as a part of expert system designed for drug discovery problem. It appears that not only MELC achieves better results than SVM but also gives some additional insights into data structure, resulting in more complex decision support system.

Includes members:
  Member "fast optimization of multithreshold entropy linear classifier":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximum entropy linear manifold for learning discriminative low dimensional representation")
  Member "weighted approach to general entropy function":
    also connected through other hyperedges:
      (none)

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.LG(Machine Learning)