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 "deep joint source channel coding for wireless image transmission"):
  Abstract: We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the ``cliff effect'', and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.

Includes members:
  Member "an autoencoder based learned image compressor description of challenge proposal by nctu":
    also connected through other hyperedges:
      (none)
  Member "fixed point convolutional neural network for real time video processing in fpga":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lightweight convolutional representations for on device natural language processing")
  Member "deep convolutional autoencoder based lossy image compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning based video coding a review and a case study")
  Member "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "ai benchmark running deep neural networks on android smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "ofdm autoencoder for end to end learning of communications systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "deep learning for joint source channel coding of text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

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.IT(Information Theory)