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: "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.

Connected through hyperedge HE-1 (induced by source paper "group testing an information theory perspective"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "understanding phase transitions via mutual information and mmse"
    - "performance of group testing algorithms with near constant tests per item"
    - "learning erd h o s r enyi random graphs via edge detecting queries"
    - "information theoretic and algorithmic thresholds for group testing"
    - "novel impossibility results for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "lower bounds for compressed sensing with generative models"
    - "solving inverse problems via auto encoders"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "the universal approximation power of finite width deep relu networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "performance of group testing algorithms with near constant tests per item"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "partition information and its transmission over boolean multi access channels"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-4 (induced by source paper "on the fundamental limits of mimo massive access communication"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "massive mimo unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "fundamental limits of low density spreading noma with fading"

Connected through hyperedge HE-5 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior"):
  with members:
    - "inference under information constraints i lower bounds from chi square contraction"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-6 (induced by source paper "rates of adaptive group testing in the linear regime"):
  with members:
    - "group testing an information theory perspective"
    - "novel impossibility results for group testing"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-7 (induced by source paper "near optimal noisy group testing via separate decoding of items"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "computationally tractable algorithms for finding a subset of non defective items from a large population"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-8 (induced by source paper "individual testing is optimal for nonadaptive group testing in the linear regime"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "novel impossibility results for group testing"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"

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)