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 "an adaptive deep learning algorithm based autoencoder for interference channels"):
  Abstract: Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive Physical Layer (PHY) design for beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE provides a new PHY paradigm with a pure data-driven and end-to-end learning based solution. However, signiﬁcant challenges are to be overcome before this approach becomes a serious contender for practical beyond-5G systems. One of such challenges is the robustness of AE under interference channels. In this paper, we ﬁrst evaluate the performance and robustness of an AE in the presence of an interference channel. Our results show that AE performs well under weak and moderate interference condition, while its performance degrades substantially under strong and very strong interference condition. We further propose a novel online adaptive deep learning (ADL) algorithm to tackle the performance issue of AE under strong and very strong interference, where level of interference can be predicted in real time for the decoding process. The performance of the proposed algorithm for diﬀerent interference scenarios is studied and compared to the existing system using a conventional DL-assist AE through an oﬄine learning method. Our results demonstrate the robustness of the proposed ADL-assist AE over the entire range of interference levels, while existing AE fail to perform in the presence of strong and very strong interference. The work proposed in this paper is an important step towards enabling AE for practical 5G and beyond communication systems with dynamic and heterogeneous interference.

Includes members:
  Member "physical adversarial attacks against end to end autoencoder communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "deep learning based channel estimation for beamspace mmwave massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "one bit ofdm receivers via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "deep reinforcement learning autoencoder with noisy feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for channel coding via neural mutual information estimation")
  Member "end to end learning of communications systems without a channel model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems")
  Member "deep learning for joint source channel coding of text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning based end to end wireless communication systems with conditional gan as unknown channel")
  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")
  Member "training of deep neural networks based on distance measures using rmsprop":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis")

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)