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: "near optimal compressive binary search"
  Abstract: We propose a simple modification to the recently proposed compressive binary search. The modification removes an unnecessary and suboptimal factor of log log n from the SNR requirement, making the procedure optimal (up to a small constant). Simulations show that the new procedure performs significantly better in practice as well. We also contrast this problem with the more well known problem of noisy binary search.

Connected through hyperedge HE-1 (induced by source paper "an overview of signal processing techniques for millimeter wave mimo systems"):
  with members:
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "joint channel and data estimation for large mimo systems with low precision adcs"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-2 (induced by source paper "channel estimation for hybrid architecture based wideband millimeter wave systems"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "auxiliary beam pair enabled aod and aoa estimation in closed loop large scale mmwave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"

Connected through hyperedge HE-3 (induced by source paper "channel estimation and hybrid precoding for millimeter wave cellular systems"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "compressive parameter estimation in awgn"
    - "near optimal adaptive compressed sensing"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "analysis of blockage effects on urban cellular networks"
    - "coding the beams improving beamforming training in mmwave communication system"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-4 (induced by source paper "near optimal adaptive compressed sensing"):
  with members:
    - "the sample complexity of search over multiple populations"
    - "boolean compressed sensing and noisy group testing"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "sequential testing for sparse recovery"
    - "convex approaches to model wavelet sparsity patterns"
    - "on the limits of sequential testing in high dimensions"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "compressive binary search"):
  with members:
    - "how well can we estimate a sparse vector"
    - "information theoretic bounds for compressed sensing"

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)