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 learning based constellation optimization for physical network coding in two way relay networks"):
  Abstract: This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node. We train DNNs such that the cross entropy loss is directly minimized, and thus it maximizes the likelihood, rather than considering the Euclidean distance of the constellations. The proposed scheme can be extended to higher level constellations with slight modification of the DNN structure. Simulation results demonstrate a significant performance gain in terms of the achievable sum rate over conventional relaying schemes. Furthermore, since our DNN demodulator directly outputs bit-wise probabilities, it is straightforward to concatenate with soft-decision channel decoding.

Includes members:
  Member "end to end deep learning of optical fiber communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback")
  Member "channel agnostic end to end learning based communication systems with conditional gan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wgan based autoencoder training over the air")
  Member "end to end learning of communications systems without a channel model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "physical layer network coding tutorial survey and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")
  Member "channel coding and decoding in a relay system operated with physical layer network coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "asymptotic analysis on ldpc bicm scheme for compute and forward relaying")

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)