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: "wireless compressive sensing for energy harvesting sensor nodes"
  Abstract: We consider the scenario in which multiple sensors send spatially correlated data to a fusion center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, we show that the recovery of this sparse signal can be formulated as a compressive sensing (CS) problem. To model the scenario in which the sensors operate with intermittently available energy that is harvested from the environment, we propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to the probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian. Besides, since the sensors have different energy harvesting rates and different sensor-to-FC distances, the FC has different receive signal-to-noise ratios (SNRs) for each sensor. This is referred to as the inhomogeneity of SNRs. Thus, the elements of the sensing matrix are also not identically distributed. For this unconventional setting, we provide theoretical guarantees on the number of measurements for reliable and computationally efficient recovery, by showing that the sensing matrix satisfies the restricted isometry property (RIP), under reasonable conditions. We then compute an achievable system delay under an allowable mean-squared-error (MSE). Furthermore, using techniques from large deviations theory, we analyze the impact of inhomogeneity of SNRs on the so-called k-restricted eigenvalues, which governs the number of measurements required for the RIP to hold. We conclude that the number of measurements required for the RIP is not sensitive to the inhomogeneity of SNRs, when the number of sensors n is large and the sparsity of the sensor data (signal) k grows slower than the square root of n. Our analysis is corroborated by extensive numerical results.

Connected through hyperedge HE-1 (induced by source paper "accelerating federated learning via momentum gradient descent"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated learning with non iid data"
    - "a survey on mobile edge computing the communication perspective"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "stochastic routing and scheduling policies for energy harvesting communication networks"):
  with members:
    - "reconstruction of correlated sources with energy harvesting constraints in delay constrained and delay tolerant communication scenarios"
    - "sensor selection and power allocation strategies for energy harvesting wireless sensor networks"
    - "energy harvesting wireless communications a review of recent advances"
    - "source channel coding under energy delay and buffer constraints"
    - "energy harvesting two hop communication networks"
    - "information and energy cooperation in cognitive radio networks"
    - "secrecy wireless information and power transfer with miso beamforming"
    - "multiuser miso beamforming for simultaneous wireless information and power transfer"

Connected through hyperedge HE-3 (induced by source paper "channel impulse response based distributed physical layer authentication"):
  with members:
    - "exploiting lack of hardware reciprocity for sender node authentication at the phy layer"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-4 (induced by source paper "graph based joint signal power restoration for energy harvesting wireless sensor networks"):
  with members:
    - "graph laplacian regularization for inverse imaging analysis in the continuous domain"
    - "throughput maximization in wireless powered communication networks"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing and parallel acquisition"):
  with members:
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "self calibration and biconvex compressive sensing"
    - "refined analysis of sparse mimo radar"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "multi view in lensless compressive imaging"
    - "an algorithm for variable density sampling with block constrained acquisition"

Connected through hyperedge HE-6 (induced by source paper "binary compressive sensing via analog fountain coding"):
  with members:
    - "near capacity adaptive analog fountain codes for wireless channels"
    - "measure what should be measured progress and challenges in compressive sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "weighted superimposed codes and constrained integer compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-7 (induced by source paper "wireless compressive sensing over fading channels with distributed sparse random projections"):
  with members:
    - "omp based joint sparsity pattern recovery under communication constraints"
    - "efficient compressive sampling of spatially sparse fields in wireless sensor networks"
    - "a probabilistic and ripless theory of compressed sensing"
    - "nonuniform sparse recovery with gaussian matrices"

Connected through hyperedge HE-8 (induced by source paper "closed form delay optimal power control for energy harvesting wireless system with finite energy storage"):
  with members:
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "decentralized delay optimal control for interference networks with limited renewable energy storage"
    - "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning"
    - "broadcasting with an energy harvesting rechargeable transmitter"
    - "sum rate optimal power policies for energy harvesting transmitters in an interference channel"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "optimum transmission policies for battery limited energy harvesting nodes"
    - "basic performance limits and tradeoffs in energy harvesting sensor nodes with finite data and energy storage"

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)