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: "a scalable and statistically robust beam alignment technique for mm wave systems"
  Abstract: Millimeter-Wave (mm-Wave) frequency bands provide an opportunity for much wider channel bandwidth compared with the traditional sub-6 GHz band. Communication at mm-Waves is, however, quite challenging due to the severe propagation path loss. To cope with this problem, directional beamforming both at the Base Station (BS) side and at the user side is necessary in order to establish a strong path conveying enough signal power. Finding such beamforming directions is referred to as the Beam Alignment (BA) and is known to be a challenging problem. This paper presents a new scheme for efficient BA, based on the estimated second order channel statistics. As a result, our proposed algorithm is highly robust to variations of the channel time-dynamics compared with other proposed approaches based on the estimation of the channel coefficients, rather than of their second-order statistics. In the proposed scheme, the BS probes the channel in the Downlink (DL) letting each user to estimate its own path direction. All the users within the BS coverage are trained simultaneously, without requiring "beam refinement" with multiple interactive rounds of Downlink/Uplink (DL/UL) transmissions, as done in other schemes. Thus, the training overhead of the proposed BA scheme is independent of the number of users in the system. We pose the channel estimation at the user side as a Compressed Sensing (CS) of a non-negative signal and use the recently developed Non-Negative Least Squares(NNLS) technique to solve it efficiently. The performance of our proposed algorithm is assessed via computer simulation in a relevant mm-Wave scenario. The results illustrate that our approach is superior to the state-of-the-art BA schemes proposed in the literature in terms of training overhead in multi-user scenarios and robustness to variations in the channel dynamics.

Connected through hyperedge HE-1 (induced by source paper "improved scaling law for activity detection in massive mimo systems"):
  with members:
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "on the gap between rip properties and sparse recovery conditions"
    - "efficient high dimensional inference in the multiple measurement vector problem"

Connected through hyperedge HE-2 (induced by source paper "a robust time domain beam alignment scheme for multi user wideband mmwave systems"):
  with members:
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"

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)