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: "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"
  Abstract: Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions. Therefore, channel state information (CSI) plays a pivotal role in the system performance. The importance of CSI is in fact growing in the upcoming 5G and beyond systems, e.g., for the implementation of massive multiple-input multiple-output (MIMO). However, the acquisition of timely and accurate CSI has long been considered as a major issue, and becomes increasingly challenging due to the need for obtaining CSI of many antenna elements in massive MIMO systems. To cope with this challenge, existing works mainly focus on exploiting linear structures of CSI, such as CSI correlations in the spatial domain, to achieve dimensionality reduction. In this article, we first systematically review the state-of-the-art on CSI structure exploitation; then extend to seek for deeper structures that enable remote CSI inference wherein a data-driven deep neural network (DNN) approach is necessary due to model inadequacy. We develop specific DNN designs suitable for CSI data. Case studies are provided to demonstrate great potential in this direction for future performance enhancement.

Connected through hyperedge HE-1 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "embb urllc resource slicing a risk sensitive approach"
    - "wireless network intelligence at the edge"
    - "federated learning for ultra reliable low latency v2v communications"
    - "channel charting locating users within the radio environment using channel state information"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "adam a method for stochastic optimization"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning based beam tracking for low latency services in vehicular networks"):
  with members:
    - "low complexity adaptive beam and channel tracking for mobile mmwave communications"
    - "online learning for position aided millimeter wave beam training"
    - "time sequence channel inference for beam alignment in vehicular networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "a survey on deep learning based techniques for modeling and estimation of massivemimo channels"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning for ul dl channel calibration in generic massive mimo systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "deep learning for csi feedback based on superimposed coding"
    - "compressed csi feedback with learned measurement matrix for mmwave massive mimo"
    - "deep learning based channel estimation for beamspace mmwave massive mimo 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)