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 ul dl channel calibration in generic massive mimo systems"
  Abstract: One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.

Connected through hyperedge HE-1 (induced by source paper "constructing deep neural networks with a priori knowledge of wireless tasks"):
  with members:
    - "optimizing wireless systems using unsupervised and reinforced unsupervised deep learning"
    - "learning to detect"
    - "learning to optimize with unsupervised learning training deep neural networks for urllc"
    - "structure of deep neural networks with a priori information in wireless tasks"
    - "an lstm network for highway trajectory prediction"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "model driven deep learning for physical layer communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (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 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"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"

Connected through hyperedge HE-3 (induced by source paper "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"):
  with members:
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "csi based outdoor localization for massive mimo experiments with a learning approach"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "massive mimo channel estimation for millimeter wave systems via matrix completion"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "structure of deep neural networks with a priori information in wireless tasks"):
  with members:
    - "learning to optimize with unsupervised learning training deep neural networks for urllc"
    - "learning optimal resource allocations in wireless systems"
    - "an lstm network for highway trajectory prediction"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "adam a method for stochastic optimization"

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)