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: "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.

Connected through hyperedge HE-1 (induced by source paper "generalized rbf kernel for incomplete data"):
  with members:
    - "partial face detection for continuous authentication"

Connected through hyperedge HE-2 (induced by source paper "maximum entropy linear manifold for learning discriminative low dimensional representation"):
  with members:
    - "fast optimization of multithreshold entropy linear classifier"
    - "on the consistency of multithreshold entropy linear classifier"
    - "cross entropy clustering"
    - "discriminative features via generalized eigenvectors"
    - "on the local correctness of l 1 minimization for dictionary learning"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-3 (induced by source paper "extreme entropy machines robust information theoretic classification"):
  with members:
    - "cross entropy clustering"
    - "cluster based rbf kernel for support vector machines"
    - "large margin distribution machine"
    - "the numpy array a structure for efficient numerical computation"

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)