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 by linear programming"
  Abstract: We give the first computationally tractable and almost optimal solution to the problem of one-bit compressed sensing, showing how to accurately recover an s-sparse vector x in R^n from the signs of O(s log^2(n/s)) random linear measurements of x. The recovery is achieved by a simple linear program. This result extends to approximately sparse vectors x. Our result is universal in the sense that with high probability, one measurement scheme will successfully recover all sparse vectors simultaneously. The argument is based on solving an equivalent geometric problem on random hyperplane tessellations.

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 "strong coresets for subspace approximation and k median in nearly linear time"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "new frameworks for offline and streaming coreset constructions"
    - "input sparsity and hardness for robust subspace approximation"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-3 (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-4 (induced by source paper "mean estimation from one bit measurements"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "on quantizer design for distributed bayesian estimation in sensor networks"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "optimality guarantees for distributed statistical estimation"

Connected through hyperedge HE-5 (induced by source paper "the estimation performance of nonlinear least squares for phase retrieval"):
  with members:
    - "phase retrieval from the norms of affine transformations"
    - "sparse phase retrieval via truncated amplitude flow"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "the generalized lasso with non linear observations"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-6 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds"):
  with members:
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "on recovery guarantees for one bit compressed sensing on manifolds"
    - "quantization for low rank matrix recovery"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "quantization and compressive sensing"
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "on one stage recovery for sigma delta quantized compressed sensing"):
  with members:
    - "memoryless scalar quantization for random frames"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"
    - "quantization of compressive samples with stable and robust recovery"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements"
    - "lower bounds for the error decay incurred by coarse quantization schemes"

Connected through hyperedge HE-8 (induced by source paper "deep one bit compressive autoencoding"):
  with members:
    - "signal recovery from 1 bit quantized noisy samples via adaptive thresholding"
    - "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi"
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to structured signal recovery"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "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)