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 "information theoretic feature transformation learning for brain interfaces"):
  Abstract: Objective:  A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking-based feature selection by any criterion, we propose to extend this focus with an information theoretic learning-driven feature transformation concept.  Methods:  We present a maximum mutual information linear transformation and a nonlinear transformation framework derived by a general definition of the feature transformation learning problem. Empirical assessments are performed based on electroencephalographic data recorded during a four class motor imagery brain–computer interface (BCI) task. Exploiting the state-of-the-art methods for initial feature vector construction, we compare the proposed approaches with conventional feature selection-based dimensionality reduction techniques, which are widely used in brain interfaces. Furthermore, for the multi-class problem, we present and exploit a hierarchical graphical model-based BCI decoding system.  Results:  Both binary and multi-class decoding analyses demonstrate significantly better performances with the proposed methods.  Conclusion:  Information theoretic feature transformations are capable of tackling potential confounders of conventional approaches in various settings.  Significance:  We argue that this concept provides significant insights to extend the focus on feature selection heuristics to a broader definition of feature transformation learning in brain interfaces.

Includes members:
  Member "adversarial deep learning in eeg biometrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial feature learning in brain interfacing an experimental study on eliminating drowsiness effects")
  Member "hierarchical graphical models for context aware hybrid brain machine interfaces":
    also connected through other hyperedges:
      (none)
  Member "multivariate extension of matrix based renyi s alpha order entropy functional":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiscale principle of relevant information for hyperspectral image classification")
  Member "transfer learning in brain computer interfaces with adversarial variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward subject invariant and class disentangled representation in bci via cross domain mutual information estimator")
  Member "estimating mutual information for discrete continuous mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a theory of usable information under computational constraints")
  Member "information theoretic semi supervised metric learning via entropy regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affinity guided geometric semi supervised metric learning")

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)