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 adaptive compressed sensing"
  Abstract: This paper proposes a simple adaptive sensing and group testing algorithm for sparse signal recovery. The algorithm, termed Compressive Adaptive Sense and Search (CASS), is shown to be near-optimal in that it succeeds at the lowest possible signal-to-noise-ratio (SNR) levels, improving on previous work in adaptive compressed sensing. Like traditional compressed sensing based on random non-adaptive design matrices, the CASS algorithm requires only k log n measurements to recover a k-sparse signal of dimension n. However, CASS succeeds at SNR levels that are a factor log n less than required by standard compressed sensing. From the point of view of constructing and implementing the sensing operation as well as computing the reconstruction, the proposed algorithm is substantially less computationally intensive than standard compressed sensing. CASS is also demonstrated to perform considerably better in practice through simulation. To the best of our knowledge, this is the first demonstration of an adaptive compressed sensing algorithm with near-optimal theoretical guarantees and excellent practical performance. This paper also shows that methods like compressed sensing, group testing, and pooling have an advantage beyond simply reducing the number of measurements or tests -- adaptive versions of such methods can also improve detection and estimation performance when compared to non-adaptive direct (uncompressed) sensing.

Connected through hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise"):
  with members:
    - "noise tolerant reliable active classification with comparison queries"
    - "a modern introduction to online learning"
    - "adaptive hard thresholding for near optimal consistent robust regression"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "efficient active learning of sparse halfspaces"
    - "active classification with comparison queries"
    - "a hitting time analysis of stochastic gradient langevin dynamics"

Connected through hyperedge HE-2 (induced by source paper "an adaptive bayesian framework for recovery of sources with structured sparsity"):
  with members:
    - "oversampled adaptive sensing with random projections analysis and algorithmic approaches"
    - "theoretical bounds on map estimation in distributed sensing networks"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-3 (induced by source paper "secure adaptive group testing"):
  with members:
    - "data aggregation over multiple access wireless sensors network"
    - "secure group testing"
    - "information theoretic bounds for adaptive sparse recovery"
    - "the capacity of non identical adaptive group testing"
    - "wiretap channel with causal state information and secure rate limited feedback"
    - "the capacity of adaptive group testing"
    - "adaptive group testing as channel coding with feedback"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-4 (induced by source paper "non uniform wavelet sampling for rf analog to information conversion"):
  with members:
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "stable and robust sampling strategies for compressive imaging"
    - "structured compressed sensing from theory to applications"
    - "noise folding in compressed sensing"
    - "recovery of sparsely corrupted signals"
    - "xampling signal acquisition and processing in union of subspaces"
    - "sub nyquist sampling of short pulses"

Connected through hyperedge HE-5 (induced by source paper "improved target acquisition rates with feedback codes"):
  with members:
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "directional initial access for millimeter wave cellular systems"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "searching with measurement dependent noise"
    - "collaborative 20 questions for target localization"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-6 (induced by source paper "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"):
  with members:
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-7 (induced by source paper "data aggregation over multiple access wireless sensors network"):
  with members:
    - "secure group testing"
    - "partition information and its transmission over boolean multi access channels"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-8 (induced by source paper "measurement adaptive sparse image sampling and recovery"):
  with members:
    - "anisotropic mesh adaptation for image representation"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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)