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: "universal compressed sensing"
  Abstract: In this paper, the problem of developing universal algorithms for compressed sensing of stochastic processes is studied. First, R\'enyi's notion of information dimension (ID) is generalized to analog stationary processes. This provides a measure of complexity for such processes and is connected to the number of measurements required for their accurate recovery. Then a minimum entropy pursuit (MEP) optimization approach is proposed, and it is proven that it can reliably recover any stationary process satisfying some mixing constraints from sufficient number of randomized linear measurements, without having any prior information about the distribution of the process. It is proved that a Lagrangian-type approximation of the MEP optimization problem, referred to as Lagrangian-MEP problem, is identical to a heuristic implementable algorithm proposed by Baron et al. It is shown that for the right choice of parameters the Lagrangian-MEP algorithm, in addition to having the same asymptotic performance as MEP optimization, is also robust to the measurement noise. For memoryless sources with a discrete-continuous mixture distribution, the fundamental limits of the minimum number of required measurements by a non-universal compressed sensing decoder is characterized by Wu et al. For such sources, it is proved that there is no loss in universal coding, and both the MEP and the Lagrangian-MEP asymptotically achieve the optimal performance.

Connected through hyperedge HE-1 (induced by source paper "on the compressibility of affinely singular random vectors"):
  with members:
    - "on the information dimension of stochastic processes"
    - "how compressible are innovation processes"
    - "rate distortion dimension of stochastic processes"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "information theoretic principles of universal discrete denoising"):
  with members:
    - "the rate distortion risk in estimation from compressed data"
    - "deducing truth from correlation"
    - "tensor decompositions for learning latent variable models"
    - "algorithms for discrete denoising under channel uncertainty"
    - "universal minimax discrete denoising under channel uncertainty"

Connected through hyperedge HE-3 (induced by source paper "on the information dimension of stochastic processes"):
  with members:
    - "the shannon lower bound is asymptotically tight"
    - "compression based compressed sensing"
    - "degrees of freedom in vector interference channels"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "compression based compressed sensing"):
  with members:
    - "from compression to compressed sensing"
    - "minimum complexity pursuit for universal compressed sensing"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "sharp recovery bounds for convex demixing with applications"
    - "sampling and recovery of pulse streams"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "stable restoration and separation of approximately sparse signals"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "new approach to bayesian high dimensional linear regression"):
  with members:
    - "minimum complexity pursuit for universal compressed sensing"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "iterative hard thresholding for compressed sensing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-6 (induced by source paper "rate distortion dimension of stochastic processes"):
  with members:
    - "compression based compressed sensing"
    - "model based compressive sensing"
    - "the simplest solution to an underdetermined system of linear equations"

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)