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: "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
  Abstract: In this work, we show that reconstructing a sparse signal from quantized compressive measurement can be achieved in an unified formalism whatever the (scalar) quantization resolution, i.e., from 1-bit to high resolution assumption. This is achieved by generalizing the iterative hard thresholding (IHT) algorithm and its binary variant (BIHT) introduced in previous works to enforce the consistency of the reconstructed signal with respect to the quantization model. The performance of this algorithm, simply called quantized IHT (QIHT), is evaluated in comparison with other approaches (e.g., IHT, basis pursuit denoise) for several quantization scenarios.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (induced by source paper "the importance of phase in complex compressive sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "uniform recovery of fusion frame structured sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-3 (induced by source paper "serial quantization for representing sparse signals"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "hardware limited task based quantization"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "spatial compressive sensing for mimo radar"
    - "group testing algorithms bounds and simulations"
    - "gespar efficient phase retrieval of sparse signals"

Connected through hyperedge HE-4 (induced by source paper "quantized compressed sensing by rectified linear units"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "robust one bit compressed sensing with partial circulant matrices"
    - "on recovery guarantees for one bit compressed sensing on manifolds"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "methods for quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

Connected through hyperedge HE-5 (induced by source paper "distributed quantization for sparse time sequences"):
  with members:
    - "serial quantization for representing sparse signals"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "compressive sensing over networks"
    - "distributed compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "analysis of hard thresholding for distributed compressed sensing with one bit measurements"):
  with members:
    - "recovering structured data from superimposed non linear measurements"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "the restricted isometry property for random block diagonal matrices"
    - "one bit compressed sensing by linear programming"
    - "distributed compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "one bit expandersketch for one bit compressed sensing"):
  with members:
    - "improved bounds for universal one bit compressive sensing"
    - "on fast decoding of high dimensional signals from one bit measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "heavy hitters via cluster preserving clustering"
    - "for all sparse recovery in near optimal time"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-8 (induced by source paper "simple classification using binary data"):
  with members:
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "binary embeddings with structured hashed projections"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

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)