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 "reasoning about generalization via conditional mutual information"):
  Abstract: We provide an information-theoretic framework for studying the generalization properties of machine learning algorithms. Our framework ties together existing approaches, including uniform convergence bounds and recent methods for adaptive data analysis. Specifically, we use Conditional Mutual Information (CMI) to quantify how well the input (i.e., the training data) can be recognized given the output (i.e., the trained model) of the learning algorithm. We show that bounds on CMI can be obtained from VC dimension, compression schemes, differential privacy, and other methods. We then show that bounded CMI implies various forms of generalization.

Includes members:
  Member "robust generalization via α mutual information":
    also connected through other hyperedges:
      (none)
  Member "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "strengthened information theoretic bounds on the generalization error":
    also connected through other hyperedges:
      (none)
  Member "tightening mutual information based bounds on generalization error":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "think global act local relating dnn generalisation and node level snr")
  Member "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "does learning require memorization a short tale about a long tail")
  Member "a new analysis of differential privacy s generalization guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "closure properties for private classification and online prediction")
  Member "pac learning with stable and private predictions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "privately answering classification queries in the agnostic pac model")
  Member "a direct sum result for the information complexity of learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information bottleneck and its applications in deep 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)