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 "high fidelity extraction of neural network models"):
  Abstract: Model extraction allows an adversary to steal a copy of a remotely deployed machine learning model given access to its predictions. Adversaries are motivated to mount such attacks for a variety of reasons, ranging from reducing their computational costs, to eliminating the need to collect expensive training data, to obtaining a copy of a model in order to find adversarial examples, perform membership inference, or model inversion attacks. #R##N#In this paper, we taxonomize the space of model extraction attacks around two objectives: \emph{accuracy}, i.e., performing well on the underlying learning task, and \emph{fidelity}, i.e., matching the predictions of the remote victim classifier on any input. #R##N#To extract a high-accuracy model, we develop a learning-based attack which exploits the victim to supervise the training of an extracted model. #R##N#Through analytical and empirical arguments, we then explain the inherent limitations that prevent any learning-based strategy from extracting a truly high-fidelity model---i.e., extracting a functionally-equivalent model whose predictions are identical to those of the victim model on all possible inputs. Addressing these limitations, we expand on prior work to develop the first practical functionally-equivalent extraction attack for direct extraction (i.e., without training) of a model's weights. #R##N#We perform experiments both on academic datasets and a state-of-the-art image classifier trained with 1 billion proprietary images. In addition to broadening the scope of model extraction research, our work demonstrates the practicality of model extraction attacks against production-grade systems.

Includes members:
  Member "s4l self supervised semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "zero shot knowledge transfer via adversarial belief matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative low bitwidth data free quantization")
  Member "a framework for the extraction of deep neural networks by leveraging public data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "overlearning reveals sensitive attributes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high accuracy and high fidelity extraction of neural networks")
  Member "on the learnability of deep random networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "mixmatch a holistic approach to semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaptive ensemble learning")
  Member "energy and policy considerations for deep learning in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "knockoff nets stealing functionality of black box models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic backdoor attacks against machine learning models")

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)