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: "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
  Abstract: Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams. Current mmWave beam training and channel estimation techniques do not normally make use of the prior beam training or channel estimation observations. Intuitively, though, the channel matrices are functions of the various elements of the environment. Learning these functions can dramatically reduce the training overhead needed to obtain the channel knowledge. In this paper, a novel solution that exploits machine learning tools, namely conditional generative adversarial networks (GAN), is developed to learn these functions between the environment and the channel covariance matrices. More specifically, the proposed machine learning model treats the covariance matrices as 2D images and learns the mapping function relating the uplink received pilots, which act as RF signatures of the environment, and these images. Simulation results show that the developed strategy efficiently predicts the covariance matrices of the large-dimensional mmWave channels with negligible training overhead.

Connected through hyperedge HE-1 (induced by source paper "enabling large intelligent surfaces with compressive sensing and deep learning"):
  with members:
    - "non stationarities in extra large scale massive mimo"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"):
  with members:
    - "channel extrapolation in fdd massive mimo theoretical analysis and numerical validation"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "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"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo ten myths and one critical question"
    - "fingerprinting based positioning in distributed massive mimo systems"

Connected through hyperedge HE-3 (induced by source paper "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "deep learning for massive mimo csi feedback"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "intra cluster characteristics of 28 ghz wireless channel in urban micro street canyon"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-4 (induced by source paper "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"):
  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"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep complex networks"
    - "an overview of multi task learning in deep neural networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"

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)