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: "non gaussian hyperplane tessellations and robust one bit compressed sensing"
  Abstract: We show that a tessellation generated by a small number of random affine hyperplanes can be used to approximate Euclidean distances between any two points in an arbitrary bounded set $T$, where the random hyperplanes are generated by subgaussian or heavy-tailed normal vectors and uniformly distributed shifts. We derive quantitative bounds on the number of hyperplanes needed for constructing such tessellations in terms of natural metric complexity measures of $T$ and the desired approximation error. Our work extends significantly prior results in this direction, which were restricted to Gaussian hyperplane tessellations of subsets of the Euclidean unit sphere. #R##N#As an application, we obtain new reconstruction results in memoryless one-bit compressed sensing with non-Gaussian measurement matrices. We show that by quantizing at uniformly distributed thresholds, it is possible to accurately reconstruct low-complexity signals from a small number of one-bit quantized measurements, even if the measurement vectors are drawn from a heavy-tailed distribution. Our reconstruction results are uniform in nature and robust in the presence of pre-quantization noise on the analog measurements as well as adversarial bit corruptions in the quantization process. Moreover we show that if the measurement matrix is subgaussian then accurate recovery can be achieved via a convex program.

Connected through hyperedge HE-1 (induced by source paper "quickly finding the best linear model in high dimensions"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning compact neural networks with regularization"
    - "robust one bit compressed sensing with non gaussian measurements"
    - "robust one bit compressed sensing with partial circulant matrices"
    - "the generalized lasso for sub gaussian measurements with dithered quantization"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-2 (induced by source paper "quantized compressed sensing by rectified linear units"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "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"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-3 (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"
    - "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"
    - "sketching for large scale learning of mixture models"

Connected through hyperedge HE-4 (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"
    - "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"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-5 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "learning compact neural networks with regularization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-6 (induced by source paper "recovering structured data from superimposed non linear measurements"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "the generalized lasso for sub gaussian measurements with dithered quantization"
    - "self calibration and biconvex compressive sensing"
    - "the generalized lasso with non linear observations"
    - "blind recovery of sparse signals from subsampled convolution"
    - "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
    - "learning without concentration"
    - "living on the edge phase transitions in convex programs with random data"

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)