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 "fundamental limits of online learning an entropic innovations viewpoint"):
  Abstract: In this paper, we examine the fundamental performance limitations of online machine learning, by viewing the online learning problem as a prediction problem with causal side information. Towards this end, we combine the entropic analysis from information theory and the innovations approach from prediction theory to derive generic lower bounds on the prediction errors as well as the conditions (in terms of, e.g., directed information) to achieve the bounds. It is seen in general that no specific restrictions have to be imposed on the learning algorithms or the distributions of the data points for the performance bounds to be valid. In addition, the cases of supervised learning, semi-supervised learning, as well as unsupervised learning can all be analyzed accordingly. We also investigate the implications of the results in analyzing the fundamental limits of generalization.

Includes members:
  Member "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises")
  Member "generic bounds on the maximum deviations in sequential prediction an information theoretic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fundamental limitations in sequential prediction and recursive algorithms l p bounds via an entropic analysis")
  Member "opening the black box of deep neural networks via information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "deep learning and the information bottleneck principle":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "universal estimation of directed information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")

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)