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 "harnessing gans for zero shot learning of new classes in visual speech recognition"):
  Abstract: Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. Recent machine learning based approaches model VSR as a classification problem; however, the scarcity of training data leads to error-prone systems with very low accuracies in predicting unseen classes. To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases. We also show that our models are language agnostic and therefore capable of seamlessly generating, using English training data, videos for a new language (Hindi). To the best of our knowledge, this is the first work to show empirical evidence of the use of GANs for generating training samples of unseen classes in the domain of VSR, hence facilitating zero-shot learning. We make the added videos for new classes publicly available along with our code.

Includes members:
  Member "modality attention for end to end audio visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "harnessing gans for addition of new classes in vsr")
  Member "low shot learning from imaginary data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting meta learning as supervised learning")
  Member "harnessing ai for speech reconstruction using multi view silent video feed":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mobivsr a visual speech recognition solution for mobile devices")
  Member "link the head to the beak zero shot learning from noisy text description at part precision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "a generative adversarial approach for zero shot learning from noisy texts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "end to end multi view lipreading":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lipreading using temporal convolutional networks")
  Member "lipreading with long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lipreading using temporal convolutional networks")
  Member "matching networks for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)