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: "sub linear time support recovery for compressed sensing using sparse graph codes"
  Abstract: We address the problem of robustly recovering the support of high-dimensional sparse signals from linear measurements in a low-dimensional subspace. We introduce a new family of sparse measurement matrices associated with low-complexity recovery algorithms. Our measurement system is designed to capture observations of the signal through sparse-graph codes, and to recover the signal by using a simple peeling decoder. As a result, we can simultaneously reduce both the measurement cost and the computational complexity. In this paper, we formally connect general sparse recovery problems in compressed sensing with sparse-graph decoding in packet-communication systems, and analyze our design in terms of the measurement cost, computational complexity and recovery performance. #R##N#Specifically, in the noiseless setting, our scheme requires $2K$ measurements asymptotically to recover the sparse support of any $K$-sparse signal with ${O}(K)$ arithmetic operations. In the presence of noise, both measurement and computational costs are ${O}(K\log^{1.\dot{3}} N)$ for recovering any $K$-sparse signal of dimension $N$. When the signal sparsity $K$ is sub-linear in the signal dimension $N$, our design achieves {\it sub-linear time support recovery}. Further, the measurement cost for noisy recovery can also be reduced to ${O}(K\log N)$ by increasing the computational complexity to near-linear time ${O}(N\log N)$. In terms of recovery performance, we show that the support of any $K$-sparse signal can be stably recovered under finite signal-to-noise ratios with probability one asymptotically.

Connected through hyperedge HE-1 (induced by source paper "sparse graph codes for non adaptive quantitative group testing"):
  with members:
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "a fast noniterative algorithm for compressive sensing using binary measurement matrices"
    - "group testing using left and right regular sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "optimal nested test plan for combinatorial quantitative group testing"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of sparse linear regressions using sparse graph codes"):
  with members:
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "nonadaptive group testing with random set of defectives"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "guess who rated this movie identifying users through subspace clustering"
    - "learning mixtures of linear classifiers"
    - "spectral experts for estimating mixtures of linear regressions"

Connected through hyperedge HE-3 (induced by source paper "fast and robust compressive phase retrieval with sparse graph codes"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "a generalized ldpc framework for robust and sublinear compressive sensing"):
  with members:
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "efficient sketches for the set query problem"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-5 (induced by source paper "a robust sub linear time r ffast algorithm for computing a sparse dft"):
  with members:
    - "sample optimal average case sparse fourier transform in two dimensions"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "nearly optimal sparse fourier transform"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-6 (induced by source paper "capacity approaching phasecode for low complexity compressive phase retrieval"):
  with members:
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "fast compressive phase retrieval from fourier measurements"
    - "reliable deniable and hidable communication over multipath networks"
    - "super sparse signals with unknown phases efficiently recovered"
    - "new conditions for sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"

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)