human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a survey on deep learning based techniques for modeling and estimation of massivemimo channels"):
  Abstract: \textit{Why does the literature consider the channel-state-information (CSI) as a 2/3-D image? What are the pros-and-cons of this consideration for accuracy-complexity trade-off?} Next generations of wireless communications require innumerable disciplines according to which a low-latency, low-traffic, high-throughput, high spectral-efficiency and low energy-consumption are guaranteed. Towards this end, the principle of massive multi-input multi-output (MaMIMO) is emerging which is conveniently deployed for millimeter wave (mmWave) bands. However, practical and realistic MaMIMO transceivers suffer from a huge range of challenging bottlenecks in design the majority of which belong to the issue of channel-estimation. Channel modeling and prediction in MaMIMO particularly suffer from computational complexity due to a high number of antenna sets and supported users. This complexity lies dominantly upon the feedback-overhead which even degrades the pilot-data trade-off in the uplink (UL)/downlink (DL) design. This comprehensive survey studies the novel deep-learning (DLg) driven techniques recently proposed in the literature which tackle the challenges discussed-above - which is for the first time. In addition, we consequently propose 7 open trends e.g. in the context of the lack of Q-learning in MaMIMO detection - for which we talk about a possible solution to the saddle-point in the 2-D pilot-data axis for a \textit{Stackelberg game} based scenario.

Includes members:
  Member "deep learning based channel estimation for massive mimo with mixed resolution adcs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "physical adversarial attacks against end to end autoencoder communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "deep learning for ul dl channel calibration in generic massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constructing deep neural networks with a priori knowledge of wireless tasks")
  Member "deep learning for tdd and fdd massive mimo mapping channels in space and frequency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "sum spectral efficiency maximization in massive mimo systems benefits from deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "power control in cellular massive mimo with varying user activity a deep learning solution")
  Member "deep learning for csi feedback based on superimposed coding":
    also connected through other hyperedges:
      (none)
  Member "compressed csi feedback with learned measurement matrix for mmwave massive mimo":
    also connected through other hyperedges:
      (none)
  Member "deep learning based channel estimation for beamspace mmwave massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")

Question: Please tell me which class the source paper 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)