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 "efficient beam training and channel estimation for millimeter wave communications under mobility"):
  Abstract: In this paper, we propose an efficient beam training technique for millimeter-wave (mmWave) communications. In the presence of the mobile users under high mobility, the conventional beam training should be performed more frequently to allow the users to acquire channel state information (CSI) accurately. Since it demands high resource overhead for beam training, we introduce the dedicated beam training protocol which sends the training beams separately to a specific high mobility user (called a target user) without changing the periodicity of the conventional beam training. The dedicated beam training does not require much resource since only a small number of the training beams are sent to the target user. In order to achieve good system performance with low training overhead, we design the optimal beam selection strategy which finds the best beamforming vectors yielding the lowest channel estimation error based on the target user's probabilistic channel information. Such dedicated beam training is combined with the new greedy channel estimator which effectively estimates the mmWave channel accounting for sparse characteristics and dynamics of the target user's channel. Our numerical evaluation demonstrates that the proposed beam training scheme can maintain good channel estimation performance with significantly less training overhead than the conventional beam training protocols.

Includes members:
  Member "compressive channel estimation and tracking for large arrays in mm wave picocells":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "an overview of signal processing techniques for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "tracking mm wave channel dynamics fast beam training strategies under mobility":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "compressed sensing for wireless communications useful tips and tricks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems")
  Member "tracking angles of departure and arrival in a mobile millimeter wave channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tracking position and orientation through millimeter wave lens mimo in 5g systems")
  Member "a survey of millimeter wave mmwave communications for 5g opportunities and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a distributed multi rf chain hybrid mmwave scheme for small cell systems")
  Member "training beam sequence design for millimeter wave mimo systems a pomdp framework":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online learning for position aided millimeter wave beam training")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")

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)