human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "phasecode fast and efficient compressive phase retrieval based on sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an adaptive sublinear time block sparse fourier transform")
  Member "sho fa robust compressive sensing with order optimal complexity measurements and bits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a generalized ldpc framework for robust and sublinear compressive sensing")
  Member "summary based structures with improved sublinear recovery for compressed sensing":
    also connected through other hyperedges:
      (none)
  Member "phase retrieval via matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "information theoretically optimal compressed sensing via spatial coupling and approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")

Question: Please tell me which class the source paper 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)