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: "learning to optimize training deep neural networks for interference management"
  Abstract: Numerical optimization has played a central role in addressing key signal processing (SP) problems. Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few. However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. In this paper, we aim at providing a new learning-based perspective to address this challenging issue. The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively—since passing the input through a DNN only requires a small number of simple operations. In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN. Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm. Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted. It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"
    - "an introduction to deep learning for the physical layer"

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"
    - "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"
    - "model driven deep learning for physical layer communications"

Connected through hyperedge HE-3 (induced by source paper "reconfigurable intelligent surface assisted multiuser miso systems exploiting deep reinforcement learning"):
  with members:
    - "beamforming design for large scale antenna arrays using deep learning"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"

Connected through hyperedge HE-4 (induced by source paper "learning to branch accelerating resource allocation in wireless networks"):
  with members:
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "energy efficient joint resource allocation and power control for d2d communications"
    - "radio resource allocation for device to device underlay communication using hypergraph theory"
    - "a survey on device to device communication in cellular networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (induced by source paper "optimizing wireless systems using unsupervised and reinforced unsupervised deep learning"):
  with members:
    - "learning to optimize with unsupervised learning training deep neural networks for urllc"
    - "unsupervised deep learning for ultra reliable and low latency communications"
    - "learning optimal resource allocations in wireless systems"
    - "model free unsupervised learning for optimization problems with constraints"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning for passive beamforming"):
  with members:
    - "beamforming design for large scale antenna arrays using deep learning"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "machine learning aided design of thinned antenna arrays for optimized network level performance"):
  with members:
    - "enabling simulation based optimization through machine learning a case study on antenna design"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "study of realistic antenna patterns in 5g mmwave cellular scenarios"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-8 (induced by source paper "accelerating generalized benders decomposition for wireless resource allocation"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "energy efficient resource allocation for mobile edge computation offloading"

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)