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 "learning problem agnostic speech representations from multiple self supervised tasks"):
  Abstract: Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure. Some recent works, however, have shown that it is possible to derive useful speech representations by employing a self-supervised encoder-discriminator approach. This paper proposes an improved self-supervised method, where a single neural encoder is followed by multiple workers that jointly solve different self-supervised tasks. The needed consensus across different tasks naturally imposes meaningful constraints to the encoder, contributing to discover general representations and to minimize the risk of learning superficial ones. Experiments show that the proposed approach can learn transferable, robust, and problem-agnostic features that carry on relevant information from the speech signal, such as speaker identity, phonemes, and even higher-level features such as emotional cues. In addition, a number of design choices make the encoder easily exportable, facilitating its direct usage or adaptation to different problems.

Includes members:
  Member "unsupervised speech representation learning using wavenet autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from abstract items to latent spaces to observed data and back compositional variational auto encoder")
  Member "a survey of recent dnn architectures on the timit phone recognition task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "speech xlnet unsupervised acoustic model pretraining for self attention networks")
  Member "unsupervised representation learning by predicting image rotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "representation learning with contrastive predictive coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "towards a universal neural network encoder for time series":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")
  Member "objects that sound":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "learning sight from sound ambient sound provides supervision for visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning")
  Member "unsupervised learning of semantic audio representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")

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)