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: "information theoretic bounds for adaptive sparse recovery"
  Abstract: We derive an information-theoretic lower bound for sample complexity in sparse recovery problems where inputs can be chosen sequentially and adaptively. This lower bound is in terms of a simple mutual information expression and unifies many different linear and nonlinear observation models. Using this formula we derive bounds for adaptive compressive sensing (CS), group testing and 1-bit CS problems. We show that adaptivity cannot decrease sample complexity in group testing, 1-bit CS and CS with linear sparsity. In contrast, we show there might be mild performance gains for CS in the sublinear regime. Our unified analysis also allows characterization of gains due to adaptivity from a wider perspective on sparse problems.

Connected through hyperedge HE-1 (induced by source paper "secure adaptive group testing"):
  with members:
    - "data aggregation over multiple access wireless sensors network"
    - "secure group testing"
    - "the capacity of non identical adaptive group testing"
    - "near optimal adaptive compressed sensing"
    - "wiretap channel with causal state information and secure rate limited feedback"
    - "the capacity of adaptive group testing"
    - "adaptive group testing as channel coding with feedback"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-2 (induced by source paper "secure group testing"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "strong converses for group testing in the finite blocklength regime"
    - "partition information and its transmission over boolean multi access channels"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-3 (induced by source paper "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "near optimal adaptive compressed sensing"
    - "compressive binary search"
    - "on the limits of sequential testing in high dimensions"
    - "lower bounds for sparse recovery"
    - "information theoretic bounds for compressed sensing"
    - "the price of certainty waterslide curves and the gap to capacity"

Connected through hyperedge HE-4 (induced by source paper "info greedy sequential adaptive compressed sensing"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "distance based and continuum fano inequalities with applications to statistical estimation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"

Connected through hyperedge HE-5 (induced by source paper "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"):
  with members:
    - "lectures on designing screening experiments"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "optimal phase transitions in compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"

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)