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: "certified data removal from machine learning models"
  Abstract: Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to "remove" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.

Connected through hyperedge HE-1 (induced by source paper "towards probabilistic verification of machine unlearning"):
  with members:
    - "formalizing data deletion in the context of the right to be forgotten"
    - "nnoculation broad spectrum and targeted treatment of backdoored dnns"
    - "machine unlearning linear filtration for logit based classifiers"
    - "bypassing backdoor detection algorithms in deep learning"
    - "hidden trigger backdoor attacks"
    - "making ai forget you data deletion in machine learning"
    - "auditing data provenance in text generation models"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-2 (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"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "enhanced convolutional neural tangent kernels"
    - "on exact computation with an infinitely wide neural net"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "machine unlearning"

Connected through hyperedge HE-3 (induced by source paper "machine unlearning"):
  with members:
    - "the seven sins of personal data processing systems under gdpr"
    - "gmail smart compose real time assisted writing"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "gazelle a low latency framework for secure neural network inference"
    - "the efficient server audit problem deduplicated re execution and the web"
    - "detecting cancer metastases on gigapixel pathology images"
    - "membership inference attacks against machine learning models"

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)