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: "deepcode feedback codes via deep learning"
  Abstract: The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes designed over several decades of research. The communication channel under consideration is the Gaussian noise channel with feedback, whose study was initiated by Shannon; feedback is known theoretically to improve reliability of communication, but no practical codes that do so have ever been successfully constructed. #R##N#We break this logjam by integrating information theoretic insights harmoniously with recurrent-neural-network based encoders and decoders to create novel codes that outperform known codes by 3 orders of magnitude in reliability. We also demonstrate several desirable properties of the codes: (a) generalization to larger block lengths, (b) composability with known codes, (c) adaptation to practical constraints. This result also has broader ramifications for coding theory: even when the channel has a clear mathematical model, deep learning methodologies, when combined with channel-specific information-theoretic insights, can potentially beat state-of-the-art codes constructed over decades of mathematical research.

Connected through hyperedge HE-1 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning"):
  with members:
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "continuous control with deep reinforcement learning"
    - "capacity of a post channel with and without feedback"
    - "capacity and coding for the ising channel with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "feedback channel communication with low precision arithmetic"):
  with members:
    - "interactive schemes for the awgn channel with noisy feedback"
    - "variations on a theme by schalkwijk and kailath"
    - "optimal feedback communication via posterior matching"
    - "concatenated coding for the awgn channel with noisy feedback"

Connected through hyperedge HE-3 (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"
    - "graph hypernetworks for neural architecture search"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-4 (induced by source paper "turbo autoencoder deep learning based channel codes for point to point communication channels"):
  with members:
    - "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"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-5 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "deep learning based polar code design"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "end to end deep learning of optical fiber communications"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for nested polar code construction"):
  with members:
    - "decoder in the loop genetic optimization based ldpc code design"
    - "decoder tailored polar code design using the genetic algorithm"
    - "ai coding learning to construct error correction codes"
    - "reinforcement learning from imperfect demonstrations"
    - "pretraining deep actor critic reinforcement learning algorithms with expert demonstrations"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "end to end learning of communications systems without a channel model"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-8 (induced by source paper "a regression approach to certain information transmission problems"):
  with members:
    - "learning to detect"
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "one bit ofdm receivers via deep learning"
    - "learning the mmse channel estimator"
    - "a model driven deep learning network for mimo detection"
    - "a two step learning and interpolation method for location based channel database"
    - "mixed adc massive mimo"

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.LG(Machine Learning)