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 "forgetting outside the box scrubbing deep networks of information accessible from input output observations"):
  Abstract: We describe a procedure for removing dependency on a cohort of training data from a trained deep network that improves upon and generalizes previous methods to different readout functions and can be extended to ensure forgetting in the activations of the network. We introduce a new bound on how much information can be extracted per query about the forgotten cohort from a black-box network for which only the input-output behavior is observed. The proposed forgetting procedure has a deterministic part derived from the differential equations of a linearized version of the model, and a stochastic part that ensures information destruction by adding noise tailored to the geometry of the loss landscape. We exploit the connections between the activation and weight dynamics of a DNN inspired by Neural Tangent Kernels to compute the information in the activations.

Includes members:
  Member "machine unlearning linear filtration for logit based classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards probabilistic verification of machine unlearning")
  Member "thieves on sesame street model extraction of bert based apis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cryptanalytic extraction of neural network models")
  Member "where is the information in a deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving generalization by controlling label noise information in neural network weights")
  Member "eternal sunshine of the spotless net selective forgetting in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "formalizing data deletion in the context of the right to be forgotten")
  Member "enhanced convolutional neural tangent kernels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "taylorized training towards better approximation of neural network training at finite width")
  Member "on exact computation with an infinitely wide neural net":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "certified data removal from machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards probabilistic verification of machine unlearning")
  Member "information in infinite ensembles of infinitely wide neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization")

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)