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 "online learning with gated linear networks"):
  Abstract: This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss. Rather than relying on non-linear transfer functions, our method gains representational power by the use of data conditioning. We state under general conditions a learnable capacity theorem that shows this approach can in principle learn any bounded Borel-measurable function on a compact subset of euclidean space; the result is stronger than many universality results for connectionist architectures because we provide both the model and the learning procedure for which convergence is guaranteed.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "deep online convex optimization with gated games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the shattered gradients problem if resnets are the answer then what is the question")
  Member "efficient second order online learning by sketching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive online learning with varying norms")
  Member "an architecture for deep hierarchical generative models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive density estimation for generative models")
  Member "compress and control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online learning in contextual bandits using gated linear networks")
  Member "exact solutions to the nonlinear dynamics of learning in deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "linear and geometric mixtures analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online learning in contextual bandits using gated linear networks")
  Member "mixing strategies in data compression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online learning in contextual bandits using gated linear networks")

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)