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: "sho fa robust compressive sensing with order optimal complexity measurements and bits"
  Abstract: Suppose x is any exactly k-sparse vector in R^n. We present a class of sparse matrices A, and a corresponding algorithm that we call SHO-FA (for Short and Fast) that, with high probability over A, can reconstruct x from Ax. The SHO-FA algorithm is related to the Invertible Bloom Lookup Tables recently introduced by Goodrich et al., with two important distinctions - SHO-FA relies on linear measurements, and is robust to noise. The SHO-FA algorithm is the first to simultaneously have the following properties: (a) it requires only O(k) measurements, (b) the bit-precision of each measurement and each arithmetic operation is O (log(n) + P) (here 2^{-P} is the desired relative error in the reconstruction of x), (c) the decoding complexity is O(k) arithmetic operations and encoding complexity is O(n) arithmetic operations, and (d) if the reconstruction goal is simply to recover a single component of x instead of all of x, with significant probability over A this can be done in constant time. All constants above are independent of all problem parameters other than the desired success probability. For a wide range of parameters these properties are information-theoretically order-optimal. In addition, our SHO-FA algorithm works over fairly general ensembles of "sparse random matrices", is robust to random noise, and (random) approximate sparsity for a large range of k. In particular, suppose the measured vector equals A(x+z)+e, where z and e correspond respectively to the source tail and measurement noise. Under reasonable statistical assumptions on z and e our decoding algorithm reconstructs x with an estimation error of O(||z||_2 +||e||_2). The SHO-FA algorithm works with high probability over A, z, and e, and still requires only O(k) steps and O(k) measurements over O(log n)-bit numbers. This is in contrast to the worst-case z model, where it is known O(k log n/k) measurements are necessary.

Connected through hyperedge HE-1 (induced by source paper "a generalized ldpc framework for robust and sublinear compressive sensing"):
  with members:
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "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-2 (induced by source paper "energy efficient decoders for compressive sensing fundamental limits and implementations"):
  with members:
    - "invertible bloom lookup tables"

Connected through hyperedge HE-3 (induced by source paper "sub linear time support recovery for compressed sensing using sparse graph codes"):
  with members:
    - "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"
    - "summary based structures with improved sublinear recovery for compressed sensing"
    - "phase retrieval via matrix completion"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "capacity achieving codes from randomness condensers"

Connected through hyperedge HE-4 (induced by source paper "super sparse signals with unknown phases efficiently recovered"):
  with members:
    - "new conditions for sparse phase retrieval"
    - "gespar efficient phase retrieval of sparse signals"
    - "near optimal phase retrieval of sparse vectors"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"

Connected through hyperedge HE-5 (induced by source paper "frantic a fast reference based algorithm for network tomography via compressive sensing"):
  with members:
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "invertible bloom lookup tables"
    - "compressive sensing over graphs"
    - "graph constrained group testing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)