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: "capacity of sparse wideband channels with partial channel feedback"
  Abstract: This paper studies the ergodic capacity of wideband multipath channels with limited feedback. Our work builds on recent results that have established the possibility of significant capacity gains in the wideband/low-SNR regime when there is perfect channel state information (CSI) at the transmitter. Furthermore, the perfect CSI benchmark gain can be obtained with the feedback of just one bit per channel coefficient. However, the input signals used in these methods are peaky, that is, they have a large peak-to-average power ratios. Signal peakiness is related to channel coherence and many recent measurement campaigns show that, in contrast to previous assumptions, wideband channels exhibit a sparse multipath structure that naturally leads to coherence in time and frequency. In this work, we first show that even an instantaneous power constraint is sufficient to achieve the benchmark gain when perfect CSI is available at the receiver. In the more realistic non-coherent setting, we study the performance of a training-based signaling scheme. We show that multipath sparsity can be leveraged to achieve the benchmark gain under both average as well as instantaneous power constraints as long as the channel coherence scales at a sufficiently fast rate with signal space dimensions. We also present rules of thumb on choosing signaling parameters as a function of the channel parameters so that the full benefits of sparsity can be realized.

Connected through hyperedge HE-1 (induced by source paper "noisy beam alignment techniques for reciprocal mimo channels"):
  with members:
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "what will 5g be"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "directional beamforming for millimeter wave mimo systems"):
  with members:
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "five disruptive technology directions for 5g"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "hybrid analog digital channel estimation and beamforming training throughput tradeoff"):
  with members:
    - "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"
    - "low rank spatial channel estimation for millimeter wave cellular systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "low complexity hybrid precoding in massive multiuser mimo systems"
    - "millimeter wave channel modeling and cellular capacity evaluation"

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)