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 "adversarial reprogramming of text classification neural networks"):
  Abstract: Adversarial Reprogramming has demonstrated success in utilizing pre-trained neural network classifiers for alternative classification tasks without modification to the original network. An adversary in such an attack scenario trains an additive contribution to the inputs to repurpose the neural network for the new classification task. While this reprogramming approach works for neural networks with a continuous input space such as that of images, it is not directly applicable to neural networks trained for tasks such as text classification, where the input space is discrete. Repurposing such classification networks would require the attacker to learn an adversarial program that maps inputs from one discrete space to the other. In this work, we introduce a context-based vocabulary remapping model to reprogram neural networks trained on a specific sequence classification task, for a new sequence classification task desired by the adversary. We propose training procedures for this adversarial program in both white-box and black-box settings. We demonstrate the application of our model by adversarially repurposing various text-classification models including LSTM, bi-directional LSTM and CNN for alternate classification tasks.

Includes members:
  Member "a practitioners guide to transfer learning for text classification using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards better social crisis data with hermes hybrid sensing for emergency management system")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "adversarial reprogramming of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial machine learning industry perspectives")
  Member "greedy attack and gumbel attack generating adversarial examples for discrete data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial examples with difficult common words for paraphrase identification")
  Member "black box attacks against rnn based malware detection algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "neural machine translation with gumbel greedy decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "crafting adversarial input sequences for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robustness verification for transformers")
  Member "seqgan sequence generative adversarial nets with policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)