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: "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
  Abstract: Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to legacy (primary) networks. In this paper, a novel wideband spectrum sensing technique referred to as multiband joint detection is introduced, which jointly detects the primary signals over multiple frequency bands rather than over one band at a time. Specifically, the spectrum sensing problem is formulated as a class of optimization problems, which maximize the aggregated opportunistic throughput of a cognitive radio system under some constraints on the interference to the primary users. By exploiting the hidden convexity in the seemingly nonconvex problems, optimal solutions can be obtained for multiband joint detection under practical conditions. The situation in which individual cognitive radios might not be able to reliably detect weak primary signals due to channel fading/shadowing is also considered. To address this issue by exploiting the spatial diversity, a cooperative wideband spectrum sensing scheme refereed to as spatial-spectral joint detection is proposed, which is based on a linear combination of the local statistics from multiple spatially distributed cognitive radios. The cooperative sensing problem is also mapped into an optimization problem, for which suboptimal solutions can be obtained through mathematical transformation under conditions of practical interest. Simulation results show that the proposed spectrum sensing schemes can considerably improve system performance. This paper establishes useful principles for the design of distributed wideband spectrum sensing algorithms in cognitive radio networks.

Connected through hyperedge HE-1 (induced by source paper "optimization of the belief propagation algorithm for distributed detection by linear data fusion techniques"):
  with members:
    - "random interruptions in cooperation for spectrum sensing in cognitive radio networks"
    - "stochastic belief propagation a low complexity alternative to the sum product algorithm"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-2 (induced by source paper "the max product algorithm viewed as linear data fusion a distributed detection scenario"):
  with members:
    - "optimization of the belief propagation algorithm for distributed detection by linear data fusion techniques"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-3 (induced by source paper "deep learning for spectrum sensing"):
  with members:
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep architectures for modulation recognition"
    - "analysis of spectrum occupancy using machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "analog to digital cognitive radio sampling detection and hardware"):
  with members:
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "cascade compressed carrier and doa estimation"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"
    - "noise folding in compressed sensing"
    - "sub nyquist sampling bridging theory and practice"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-5 (induced by source paper "improved target acquisition rates with feedback codes"):
  with members:
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "directional initial access for millimeter wave cellular systems"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "searching with measurement dependent noise"
    - "collaborative 20 questions for target localization"
    - "near optimal adaptive compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "energy efficiency of distributed signal processing in wireless networks a cross layer analysis"):
  with members:
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "energy efficient design of mimo heterogeneous networks with wireless backhaul"
    - "group blind detection with very large antenna arrays in the presence of pilot contamination"
    - "distributed coordination of self organizing mechanisms in communication networks"
    - "physical layer security in downlink multi antenna cellular networks"
    - "distributed pareto optimization via diffusion strategies"
    - "equivalence and comparison of heterogeneous cellular networks"
    - "analytical modeling of uplink cellular networks"

Connected through hyperedge HE-7 (induced by source paper "achieving autonomous compressive spectrum sensing for cognitive radios"):
  with members:
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "wideband spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-8 (induced by source paper "opportunistic spectrum sharing in dynamic access networks deployment challenges optimizations solutions and open issues"):
  with members:
    - "exploiting self interference suppression for improved spectrum awareness efficiency in cognitive radio systems"
    - "channel assignment with access contention resolution for cognitive radio networks"
    - "cognitive mimo radio"

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)