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: "scaling deep learning based decoding of polar codes via partitioning"
  Abstract: The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a high-level of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-the-art polar decoders such as successive cancellation list and belief propagation decoding.

Connected through hyperedge HE-1 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-2 (induced by source paper "molecule property prediction and classification with graph hypernetworks"):
  with members:
    - "hyper graph network decoders for block codes"
    - "incidence networks for geometric deep learning"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "relational pooling for graph representations"
    - "deepcode feedback codes via deep learning"
    - "graph hypernetworks for neural architecture search"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-3 (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"
    - "end to end learning of communications systems without a channel model"
    - "belief propagation list decoding of polar codes"
    - "an introduction to deep learning for the physical layer"
    - "sparse graphs for belief propagation decoding of polar codes"

Connected through hyperedge HE-4 (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"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "don t decay the learning rate increase the batch size"
    - "comparative study of cnn and rnn for natural language processing"

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"
    - "end to end learning of communications systems without a channel model"
    - "a model driven deep learning network for mimo detection"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning from the syndrome"
    - "deep learning methods for improved decoding of linear codes"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "an introduction to deep learning for the physical layer"
    - "deep complex networks"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-7 (induced by source paper "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "deep learning in physical layer communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "model driven deep learning for physical layer communications"
    - "a model driven deep learning network for mimo detection"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-8 (induced by source paper "a gated hypernet decoder for polar codes"):
  with members:
    - "hyper graph network decoders for block codes"
    - "molecule property prediction and classification with graph hypernetworks"
    - "neural belief propagation decoding of crc polar concatenated codes"
    - "graph hypernetworks for neural architecture search"
    - "deep learning methods for improved decoding of linear codes"
    - "on deep learning based channel decoding"
    - "smash one shot model architecture search through hypernetworks"
    - "learning to decode linear codes using deep learning"

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)