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 "prediction poisoning utility constrained defenses against model stealing attacks"):
  Abstract: With the advances of ML models in recent years, we are seeing an increasing number of real-world commercial applications and services e.g., autonomous vehicles, medical equipment, web APIs emerge. Recent advances in model functionality stealing attacks via black-box access (i.e., inputs in, predictions out) threaten the business model of such ML applications, which require a lot of time, money, and effort to develop. In this paper, we address the issue by studying defenses for model stealing attacks, largely motivated by a lack of effective defenses in literature. We work towards the first defense which introduces targeted perturbations to the model predictions under a utility constraint. Our approach introduces the perturbations targeted towards manipulating the training procedure of the attacker. We evaluate our approach on multiple datasets and attack scenarios across a range of utility constrains. Our results show that it is indeed possible to trade-off utility (e.g., deviation from original prediction, test accuracy) to significantly reduce effectiveness of model stealing attacks.

Includes members:
  Member "knockoff nets stealing functionality of black box models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "turning your weakness into a strength watermarking deep neural networks by backdooring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "have you stolen my model evasion attacks against deep neural network watermarking techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples")
  Member "ml leaks model and data independent membership inference attacks and defenses on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "poison frogs targeted clean label poisoning attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "deep mutual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "emnist an extension of mnist to handwritten letters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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)