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 "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing"):
  Abstract: The advantages of adaptivity and feedback are of immense interest in signal processing and communication with many positive and negative results. Although it is established that adaptivity does not offer substantial reductions in minimax mean square error for a fixed number of measurements, existing results have shown several advantages of adaptivity in complexity of reconstruction, accuracy of support detection, and gain in signal-to-noise ratio, under constraints on sensing energy. Sensing energy has often been measured in terms of the Frobenius Norm of the sensing matrix. This paper uses a different metric that we call the $l_0$ cost of a sensing matrix-- to quantify the complexity of sensing. Thus sparse sensing matrices have a lower cost. We derive information-theoretic lower bounds on the $l_0$ cost that hold for any non-adaptive sensing strategy. We establish that any non-adaptive sensing strategy must incur an $l_0$ cost of $\Theta\left( N \log_2(N)\right) $ to reconstruct an $N$-dimensional, one--sparse signal when the number of measurements are limited to $\Theta\left(\log_2 (N)\right)$. In comparison, bisection-type adaptive strategies only require an $l_0$ cost of at most $\mathcal{O}(N)$ for an equal number of measurements. The problem has an interesting interpretation as a sphere packing problem in a multidimensional space, such that all the sphere centres have minimum non-zero co-ordinates. We also discuss the variation in $l_0$ cost as the number of measurements increase from $\Theta\left(\log_2 (N)\right)$ to $\Theta\left(N\right)$.

Includes members:
  Member "info greedy sequential adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an active sequential xampling detector for spectrum sensing")
  Member "information theoretic bounds for adaptive sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure adaptive group testing")
  Member "near optimal adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "compressive binary search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constrained adaptive sensing")
  Member "on the limits of sequential testing in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed anomaly detection with multiple mixed observations")
  Member "lower bounds for sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "querying a matrix through matrix vector products")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random access for massive machine type communications")
  Member "the price of certainty waterslide curves and the gap to capacity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "information capacity of an energy harvesting sensor node")

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)