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: "a model driven deep learning network for mimo detection"
  Abstract: In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding the iterative algorithm. Some trainable parameters are optimized through deep learning techniques to improve the detection performance. Since the number of trainable variables of the network is equal to that of the layers, the network can be easily trained within a very short time. Furthermore, the network can handle time-varying channel with only a single training. Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.

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 "theoretical interpretation of learned step size in deep unfolded gradient descent"):
  with members:
    - "chebyshev inertial landweber algorithm for linear inverse problems"
    - "degrees of freedom analysis of unrolled neural networks"
    - "trainable ista for sparse signal recovery"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "neural proximal gradient descent for compressive imaging"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "amp inspired deep networks for sparse linear inverse problems"

Connected through hyperedge HE-4 (induced by source paper "deep hypernetwork based mimo detection"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "the roadmap to 6g ai empowered wireless networks"
    - "learning feed forward one shot learners"
    - "optimality of large mimo detection via approximate message passing"
    - "adam a method for stochastic optimization"
    - "mixed integer quadratic programming is in np"

Connected through hyperedge HE-5 (induced by source paper "learning aided deep path prediction for sphere decoding in large mimo systems"):
  with members:
    - "learning to detect"
    - "no downlink pilots are needed in tdd massive mimo"

Connected through hyperedge HE-6 (induced by source paper "model driven beamforming neural networks"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"

Connected through hyperedge HE-7 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "model driven deep learning for physical layer communications"
    - "amp inspired deep networks for sparse linear inverse problems"

Connected through hyperedge HE-8 (induced by source paper "neural network optimized 1 bit precoding for massive mu mimo"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "multilevel mimo detection with deep learning"
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "quantized constant envelope precoding with psk and qam signaling"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "1 bit massive mu mimo precoding in vlsi"
    - "quantized precoding for multi antenna downlink channels with magiq"

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)