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: "uncertainty relations for shift invariant analog signals"
  Abstract: The past several years have witnessed a surge of research investigating various aspects of sparse representations and compressed sensing. Most of this work has focused on the finite-dimensional setting in which the goal is to decompose a finite-length vector into a given finite dictionary. Underlying many of these results is the conceptual notion of an uncertainty principle: a signal cannot be sparsely represented in two different bases. Here, we extend these ideas and results to the analog, infinite-dimensional setting by considering signals that lie in a finitely generated shift-invariant (SI) space. This class of signals is rich enough to include many interesting special cases such as multiband signals and splines. By adapting the notion of coherence defined for finite dictionaries to infinite SI representations, we develop an uncertainty principle similar in spirit to its finite counterpart. We demonstrate tightness of our bound by considering a bandlimited lowpass train that achieves the uncertainty principle. Building upon these results and similar work in the finite setting, we show how to find a sparse decomposition in an overcomplete dictionary by solving a convex optimization problem. The distinguishing feature of our approach is the fact that even though the problem is defined over an infinite domain with infinitely many variables and constraints, under certain conditions on the dictionary spectrum our algorithm can find the sparsest representation by solving a finite-dimensional problem.

Connected through hyperedge HE-1 (induced by source paper "sparse signal recovery in hilbert spaces"):
  with members:
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "recovery of sparsely corrupted signals"
    - "sparse recovery from combined fusion frame measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (induced by source paper "structured compressed sensing from theory to applications"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "xampling in ultrasound imaging"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "noise folding in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "reduced dimension multiuser detection"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "structured compressed sensing from theory to applications"
    - "large system analysis of multiuser detection with an unknown number of users a high snr approach"
    - "noise folding in compressed sensing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "support recovery of sparse signals"

Connected through hyperedge HE-4 (induced by source paper "xampling signal acquisition and processing in union of subspaces"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "xampling compressed sensing of analog signals"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "recovering signals from lowpass data"
    - "sub nyquist sampling of short pulses"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-5 (induced by source paper "multichannel sampling of pulse streams at the rate of innovation"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "xampling signal acquisition and processing in union of subspaces"
    - "recovering signals from lowpass data"
    - "sub nyquist sampling of short pulses"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "expected rip conditioning of the modulated wideband converter"

Connected through hyperedge HE-6 (induced by source paper "sub nyquist sampling of short pulses"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "sampling and recovery of pulse streams"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"
    - "recovering signals from lowpass data"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "block sparse signals uncertainty relations and efficient recovery"):
  with members:
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "model based compressive sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "iterative hard thresholding for compressed sensing"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-8 (induced by source paper "from theory to practice sub nyquist sampling of sparse wideband analog signals"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "efficient sampling of sparse wideband analog signals"
    - "multirate synchronous sampling of sparse multiband signals"

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)