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 "think global act local relating dnn generalisation and node level snr"):
  Abstract: The reasons behind good DNN generalisation remain an open question. In this paper we explore the problem by looking at the Signal-to-Noise Ratio of nodes in the network. Starting from information theory principles, it is possible to derive an expression for the SNR of a DNN node output. Using this expression we construct figures-of-merit that quantify how well the weights of a node optimise SNR (or, equivalently, information rate). Applying these figures-of-merit, we give examples indicating that weight sets that promote good SNR performance also exhibit good generalisation. In addition, we are able to identify the qualities of weight sets that exhibit good SNR behaviour and hence promote good generalisation. This leads to a discussion of how these results relate to network training and regularisation. Finally, we identify some ways that these observations can be used in training design.

Includes members:
  Member "information bottleneck and its applications in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model primitive hierarchical lifelong reinforcement learning")
  Member "tightening mutual information based bounds on generalization error":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the distance between two neural networks and the stability of learning")
  Member "on variational bounds of mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "entropy and mutual information in models of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "putting an end to end to end gradient isolated learning of representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "chaining mutual information and tightening generalization bounds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "theory iiib generalization in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural 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)