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: "methods for quantized compressed sensing"
  Abstract: In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements. We also introduce two new greedy approaches for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP) and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding (AOP-QIHT). We compare the performance of greedy quantized compressed sensing algorithms for a given bit-depth, sparsity, and noise level.

Connected through hyperedge HE-1 (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"
    - "the generalized lasso with non linear observations"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "superset technique for approximate recovery in one bit compressed sensing"):
  with members:
    - "improved bounds for universal one bit compressive sensing"
    - "nonadaptive group testing with random set of defectives"
    - "group testing algorithms bounds and simulations"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "boolean compressed sensing and noisy group testing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-3 (induced by source paper "quantization in compressive sensing a signal processing approach"):
  with members:
    - "quantization and compressive sensing"
    - "noise folding in compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "optimizing quantization for lasso recovery"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "quantization and compressive sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "message passing de quantization with applications to compressed sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "frame permutation quantization"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

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)