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 "deepjscc f deep joint source channel coding of images with feedback"):
  Abstract: We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity achieving channel coding achieves the optimal performance. Although it is well known that separation is not optimal in the practical finite blocklength regime, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separate schemes. Inspired by the recent success of deep learning methods for JSCC, we investigate how noiseless or noisy channel output feedback can be incorporated into the transmission system to improve the reconstruction quality at the receiver. We introduce an autoencoder-based deep JSCC scheme that exploits the channel output feedback, and provides considerable improvements in terms of the end-to-end reconstruction quality for fixed length transmission, or in terms of the average delay for variable length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding-based designs.

Includes members:
  Member "successive refinement of images with deep joint source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning in the air")
  Member "deep learning based polar code design":
    also connected through other hyperedges:
      (none)
  Member "joint autoregressive and hierarchical priors for learned image compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss")
  Member "end to end deep learning of optical fiber communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for distributed optimization applications to wireless resource management")
  Member "deepcode feedback codes via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "deep joint source channel coding for wireless image transmission":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "deep learning for joint source channel coding of text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels")

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)