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 over fading channels with distributed sparse random projections"
  Abstract: We address the problem of recovering a sparse signal observed by a resource constrained wireless sensor network under channel fading. Sparse random matrices are exploited to reduce the communication cost in forwarding information to a fusion center. The presence of channel fading leads to inhomogeneity and non Gaussian statistics in the effective measurement matrix that relates the measurements collected at the fusion center and the sparse signal being observed. We analyze the impact of channel fading on nonuniform recovery of a given sparse signal by leveraging the properties of heavy-tailed random matrices. We quantify the additional number of measurements required to ensure reliable signal recovery in the presence of nonidentical fading channels compared to that is required with identical Gaussian channels. Our analysis provides insights into how to control the probability of sensor transmissions at each node based on the channel fading statistics in order to minimize the number of measurements collected at the fusion center for reliable sparse signal recovery. We further discuss recovery guarantees of a given sparse signal with any random projection matrix where the elements are sub-exponential with a given sub-exponential norm. Numerical results are provided to corroborate the theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "on analog gradient descent learning over multiple access fading channels"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "optimizing pipelined computation and communication for latency constrained edge learning"
    - "federated learning over wireless fading channels"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "spectrum and energy efficient multiple access for detection in wireless sensor networks"

Connected through hyperedge HE-2 (induced by source paper "fusion based cooperative support identification for compressive networked sensing"):
  with members:
    - "compressed sensing for wireless communications useful tips and tricks"
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "recovering compressively sampled signals using partial support information"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (induced by source paper "spectrum and energy efficient multiple access for detection in wireless sensor networks"):
  with members:
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "active anomaly detection in heterogeneous processes"
    - "exploiting spatial correlation in energy constrained distributed detection"
    - "asymptotically optimal anomaly detection via sequential testing"

Connected through hyperedge HE-4 (induced by source paper "energy efficient sensor censoring for compressive distributed sparse signal recovery"):
  with members:
    - "dass distributed adaptive sparse sensing"
    - "exploiting sparse dynamics for bandwidth reduction in cooperative sensing systems"

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"
    - "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"
    - "wireless compressive sensing for energy harvesting sensor nodes"
    - "an algorithm for variable density sampling with block constrained acquisition"

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)