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 "limits on support recovery with probabilistic models an information theoretic framework"):
  Abstract: The support recovery problem consists of determining a sparse subset of a set of variables that is relevant in generating a set of observations, and arises in a diverse range of settings such as compressive sensing, and subset selection in regression, and group testing. In this paper, we take a unified approach to support recovery problems, considering general probabilistic models relating a sparse data vector to an observation vector. We study the information-theoretic limits of both exact and partial support recovery, taking a novel approach motivated by thresholding techniques in channel coding. We provide general achievability and converse bounds characterizing the trade-off between the error probability and number of measurements, and we specialize these to the linear, 1-bit, and group testing models. In several cases, our bounds not only provide matching scaling laws in the necessary and sufficient number of measurements, but also sharp thresholds with matching constant factors. Our approach has several advantages over previous approaches: For the achievability part, we obtain sharp thresholds under broader scalings of the sparsity level and other parameters (e.g., signal-to-noise ratio) compared to several previous works, and for the converse part, we not only provide conditions under which the error probability fails to vanish, but also conditions under which it tends to one.

Includes members:
  Member "converse bounds for noisy group testing with arbitrary measurement matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group testing an information theory perspective")
  Member "asymptotics of fingerprinting and group testing tight bounds from channel capacities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group testing an information theory perspective")
  Member "signal estimation with additive error metrics in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "group testing algorithms bounds and simulations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "sparse signal processing with linear and non linear observations a unified shannon theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models")
  Member "the capacity of adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the all or nothing behavior of bernoulli group testing")

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)