human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "eternal sunshine of the spotless net selective forgetting in deep networks"
  Abstract: We explore the problem of selectively forgetting a particular set of data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights, computed with no knowledge of the random seed used for training, is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "thieves on sesame street model extraction of bert based apis"
    - "where is the information in a deep neural network"
    - "enhanced convolutional neural tangent kernels"
    - "on exact computation with an infinitely wide neural net"
    - "certified data removal from machine learning models"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "machine unlearning"

Connected through hyperedge HE-2 (induced by source paper "formalizing data deletion in the context of the right to be forgotten"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "making ai forget you data deletion in machine learning"
    - "proving erasure"
    - "machine unlearning"
    - "algorithms that remember model inversion attacks and data protection law"
    - "machine learning models that remember too much"

Question: Please tell me which class the center node 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)