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 "membership encoding for deep learning"):
  Abstract: Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires massive labeled data and advanced learning algorithms. The resulting models are considered as intellectual property of the model owners and their copyright should be protected. Also, MLaaS needs to be trusted not to embed secret information about the training data into the model, such that it could be later retrieved when the model is deployed. #R##N#In this paper, we present \emph{membership encoding} for training deep neural networks and encoding the membership information, i.e. whether a data point is used for training, for a subset of training data. Membership encoding has several applications in different scenarios, including robust watermarking for model copyright protection, and also the risk analysis of stealthy data embedding privacy attacks. Our encoding algorithm can determine the membership of significantly redacted data points, and is also robust to model compression and fine-tuning. It also enables encoding a significant fraction of the training set, with negligible drop in the model's prediction accuracy.

Includes members:
  Member "exploiting unintended feature leakage in collaborative learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "deepmarks a digital fingerprinting framework for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples")
  Member "the secret sharer measuring unintended neural network memorization extracting secrets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system")
  Member "turning your weakness into a strength watermarking deep neural networks by backdooring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "understanding membership inferences on well generalized learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "deepsigns a generic watermarking framework for ip protection of deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "survey of attacks and defenses on edge deployed neural networks")
  Member "knock knock who s there membership inference on aggregate location data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)