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: "one bit compressed sensing with non gaussian measurements"
  Abstract: In one-bit compressed sensing, previous results state that sparse signals may be robustly recovered when the measurements are taken using Gaussian random vectors. In contrast to standard compressed sensing, these results are not extendable to natural non-Gaussian distributions without further assumptions, as can be demonstrated by simple counter-examples. We show that approximately sparse signals that are not extremely sparse can be accurately reconstructed from single-bit measurements sampled according to a sub-gaussian distribution, and the reconstruction comes as the solution to a convex program.

Connected through hyperedge HE-1 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-2 (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-3 (induced by source paper "memoryless scalar quantization for random frames"):
  with members:
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-4 (induced by source paper "phase retrieval without small ball probability assumptions"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "a partial derandomization of phaselift using spherical designs"
    - "phase retrieval with application to optical imaging"
    - "low rank matrix recovery from rank one measurements"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"

Connected through hyperedge HE-5 (induced by source paper "robust one bit compressed sensing with partial circulant matrices"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "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"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"

Connected through hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces"):
  with members:
    - "efficient learning of linear separators under bounded noise"
    - "learning with a drifting target concept"
    - "beyond disagreement based agnostic active learning"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "active and passive learning of linear separators under log concave distributions"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "signal recovery from 1 bit quantized noisy samples via adaptive thresholding"):
  with members:
    - "optimized transmission for consensus in wireless sensor networks"
    - "one bit massive mimo channel estimation and high order modulations"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "non gaussian hyperplane tessellations and robust one bit compressed sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "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"

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)