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: "detection algorithms for communication systems using deep learning"
  Abstract: The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel, which dictates the relationship between the transmitted and the received signals. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, it is not possible to accurately model this relationship. In these scenarios, because of the lack of mathematical channel models, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by borrowing tools from deep learning, it is possible to train detectors that perform well, without any knowledge of the underlying channel models. We evaluate these algorithms using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a simple detector that was used in previous works, which also did not assume any knowledge of the channel.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming"):
  with members:
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "cnn based signal detection for banded linear systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "learning to detect"
    - "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"

Connected through hyperedge HE-3 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-4 (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"
    - "scaling deep learning based decoding of polar codes via partitioning"

Connected through hyperedge HE-5 (induced by source paper "learning to detect"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "learning approximate neural estimators for wireless channel state information"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "rnn decoding of linear block codes"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "onsager corrected deep learning for sparse linear inverse problems"

Connected through hyperedge HE-6 (induced by source paper "supervised learning for multi hop mu mimo communications with one bit transceivers"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "multipair massive mimo relaying systems with one bit adc s and dac s"
    - "one bit sphere decoding for uplink massive mimo systems with one bit adcs"
    - "an introduction to deep learning for the physical layer"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "quantized massive mu mimo ofdm uplink"

Connected through hyperedge HE-7 (induced by source paper "deep learning aided tabu search detection for large mimo systems"):
  with members:
    - "learning to detect"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-8 (induced by source paper "on recurrent neural networks for sequence based processing in communications"):
  with members:
    - "trellis networks for sequence modeling"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an introduction to deep learning for the physical layer"
    - "lstm a search space odyssey"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)