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 "training beam sequence design for millimeter wave mimo systems a pomdp framework"):
  Abstract: In this paper, adaptive training beam sequence design for efficient channel estimation in large millimeter-wave(mmWave) multiple-input multiple-output (MIMO) channels is considered. By exploiting the sparsity in large mmWave MIMO channels and imposing a Markovian random walk assumption on the movement of the receiver and reflection clusters, the adaptive training beam sequence design and channel estimation problem is formulated as a partially observableMarkov decision process (POMDP) problem that finds non-zero bins in a two-dimensional grid. Under the proposed POMDP framework, optimal and suboptimal adaptive training beam sequence design policies are derived. Furthermore, a very fast suboptimal greedy algorithm is developed based on a newly proposed reduced sufficient statistic to make the computational complexity of the proposed algorithm low to a level for practical implementation. Numerical results are provided to evaluate the performance of the proposed training beam design method. Numerical results show that the proposed training beam sequence design algorithms yield good performance.

Includes members:
  Member "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation")
  Member "pilot signal design for massive mimo systems a received signal to noise ratio based approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi cell multiuser massive mimo networks user capacity analysis and pilot design")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "pilot beam pattern design for channel estimation in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure massive mimo communication with low resolution dacs")
  Member "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "millimeter wave mimo channel tracking systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal precoders for tracking the aod and aoa of a mmwave path")
  Member "millimeter wave beamforming for wireless backhaul and access in small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning beam codebooks with neural networks towards environment aware mmwave mimo")
  Member "incremental pruning a simple fast exact method for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")

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)