human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "end to end learning of communications systems without a channel model"
  Abstract: The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm iterates between supervised training of the receiver and reinforcement learning -based training of the transmitter. We demonstrate that this approach works as well as fully supervised methods on additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF) channels. Surprisingly, while our method converges slower on AWGN channels than supervised training, it converges faster on RBF channels. Our results are a first step towards learning of communications systems over any type of channel without prior assumptions.

Connected through hyperedge HE-1 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems"):
  with members:
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "deep learning in neural networks"
    - "energy based modulation for noncoherent massive simo systems"
    - "performance analysis of energy detection based massive simo"

Connected through hyperedge HE-2 (induced by source paper "deep learning based polar code design"):
  with members:
    - "decoder in the loop genetic optimization based ldpc code design"
    - "decoder tailored polar code design using the genetic algorithm"
    - "design of polar codes in 5g new radio"
    - "belief propagation decoding of polar codes on permuted factor graphs"
    - "belief propagation list decoding of polar codes"
    - "an introduction to deep learning for the physical layer"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "sparse graphs for belief propagation decoding of polar codes"

Connected through hyperedge HE-3 (induced by source paper "turbo autoencoder deep learning based channel codes for point to point communication channels"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep learning methods for improved decoding of linear codes"
    - "neural joint source channel coding"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "an introduction to deep learning for the physical layer"
    - "don t decay the learning rate increase the batch size"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels"):
  with members:
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "one bit ofdm receivers via deep learning"
    - "deep reinforcement learning autoencoder with noisy feedback"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"
    - "training of deep neural networks based on distance measures using rmsprop"
    - "deep learning based mimo communications"

Connected through hyperedge HE-5 (induced by source paper "deep learning based end to end wireless communication systems with conditional gan as unknown channel"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a model driven deep learning network for mimo detection"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"
    - "deep learning for joint source channel coding of text"

Connected through hyperedge HE-6 (induced by source paper "an overview of data importance aware radio resource management for edge machine learning"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "deepcode feedback codes via deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "wireless data acquisition for edge learning data importance aware retransmission"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-7 (induced by source paper "data driven network simulation for performance analysis of anticipatory vehicular communication systems"):
  with members:
    - "towards 6g networks use cases and technologies"
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "heterogeneous v2v communications in multi link and multi rat vehicular networks"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "toward intelligent vehicular networks a machine learning framework"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "deep learning in physical layer communications"

Connected through hyperedge HE-8 (induced by source paper "deep learning based precoding for the mimo gaussian wiretap channel"):
  with members:
    - "a rotation based method for precoding in gaussian mimome channels"
    - "interplay between noma and other emerging technologies a survey"
    - "deep learning for the gaussian wiretap channel"
    - "deep learning in mobile and wireless networking a survey"
    - "an introduction to deep learning for the physical layer"
    - "optimal beamforming for gaussian mimo wiretap channels with two transmit antennas"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Question: Please tell me which class the center node 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)