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: "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
  Abstract: This paper proposes a novel neural network architecture, that we call an auto-precoder, and a deep-learning based approach that jointly senses the millimeter wave (mmWave) channel and designs the hybrid precoding matrices with only a few training pilots. More specifically, the proposed machine learning model leverages the prior observations of the channel to achieve two objectives. First, it optimizes the compressive channel sensing vectors based on the surrounding environment in an unsupervised manner to focus the sensing power on the most promising spatial directions. This is enabled by a novel neural network architecture that accounts for the constraints on the RF chains and models the transmitter/receiver measurement matrices as two complex-valued convolutional layers. Second, the proposed model learns how to construct the RF beamforming vectors of the hybrid architectures directly from the projected channel vector (the received sensing vector). The auto-precoder neural network that incorporates both the channel sensing and beam prediction is trained end-to-end as a multi-task classification problem. Thanks to this design methodology that leverages the prior channel observations and the implicit awareness about the surrounding environment/user distributions, the proposed approach significantly reduces the training overhead compared to classical (non-machine learning) solutions. For example, for a system of 64 transmit and 64 receive antennas, with 3 RF chains at both sides, the proposed solution needs only 8 or 16 channel training pilots to directly predict the RF beamforming/combining vectors of the hybrid architectures and achieve near-optimal achievable rates. This highlights a promising solution for the channel estimation and hybrid precoding design problem in mmWave and massive MIMO systems.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for intelligent reflecting surfaces towards standalone operation"):
  with members:
    - "reconfigurable intelligent surface empowered 6g wireless communications challenges and opportunities"
    - "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"
    - "holographic mimo surfaces for 6g wireless networks opportunities challenges and trends"
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"

Connected through hyperedge HE-2 (induced by source paper "learning beam codebooks with neural networks towards environment aware mmwave mimo"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "beam codebook design for 5g mmwave terminals"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "deep complex networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-3 (induced by source paper "deep transfer learning based downlink channel prediction for fdd massive mimo systems"):
  with members:
    - "fdd massive mimo based on efficient downlink channel reconstruction"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "decaying momentum helps neural network training"
    - "learning to transfer unsupervised meta domain translation"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "model aided deep neural network for source number detection"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "viwi a deep learning dataset framework for vision aided wireless communications"

Connected through hyperedge HE-4 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning and gaussian process based band assignment in dual band systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "modeling and analyzing millimeter wave cellular systems"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"

Connected through hyperedge HE-5 (induced by source paper "viwi a deep learning dataset framework for vision aided wireless communications"):
  with members:
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-6 (induced by source paper "millimeter wave base stations with cameras vision aided beam and blockage prediction"):
  with members:
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "viwi a deep learning dataset framework for vision aided wireless communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "deep residual learning for image recognition"

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)