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: "making ai forget you data deletion in machine learning"
  Abstract: Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used -- the EU's Right To Be Forgotten regulation is an example of this effort. In this paper we initiate a framework studying what to do when it is no longer permissible to deploy models derivative from specific user data. In particular, we formulate the problem of how to efficiently delete individual data points from trained machine learning models. For many standard ML models, the only way to completely remove an individual's data is to retrain the whole model from scratch on the remaining data, which is often not computationally practical. We investigate algorithmic principles that enable efficient data deletion in ML. For the specific setting of k-means clustering, we propose two provably deletion efficient algorithms which achieve an average of over 100X improvement in deletion efficiency across 6 datasets, while producing clusters of comparable statistical quality to a canonical k-means++ baseline.

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"
    - "certified data removal from machine learning models"
    - "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 "a distributional framework for data valuation"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "who s responsible jointly quantifying the contribution of the learning algorithm and training data"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "a marketplace for data an algorithmic solution"
    - "a unified approach to interpreting model predictions"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (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"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "proving erasure"
    - "machine unlearning"
    - "algorithms that remember model inversion attacks and data protection law"
    - "machine learning models that remember too much"

Connected through hyperedge HE-4 (induced by source paper "secure federated submodel learning"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "deep leakage from gradients"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "consistent and accurate frequency oracles under local differential privacy"
    - "federated learning of out of vocabulary words"

Connected through hyperedge HE-5 (induced by source paper "certified data removal from machine learning models"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep learning with dynamic computation graphs"

Connected through hyperedge HE-6 (induced by source paper "analyzing privacy loss in updates of natural language models"):
  with members:
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "auditing data provenance in text generation models"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "attention is all you need"
    - "learning differentially private recurrent language models"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "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)