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: "wideband spectrum sensing for cognitive radio networks a survey"
  Abstract: Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multi-channel sub-Nyquist sampling techniques.

Connected through hyperedge HE-1 (induced by source paper "20 years of evolution from cognitive to intelligent communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning in physical layer communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing"):
  with members:
    - "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "xampling signal acquisition and processing in union of subspaces"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-3 (induced by source paper "sparse representation for wireless communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressed channel feedback for correlated massive mimo systems"
    - "compressive sensing based differential channel feedback for massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"

Connected through hyperedge HE-4 (induced by source paper "connectivity of underlay cognitive radio networks with directional antennas"):
  with members:
    - "on the performance analysis of underlay cognitive radio systems a deployment perspective"
    - "stochastic geometry modeling and performance evaluation of mmwave cellular communications"
    - "a geometrical based throughput bound analysis for device to device communications in cellular networks"
    - "directional antennas improve the link connectivity of interference limited ad hoc networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-5 (induced by source paper "multi user communication networks a coordinated multi armed bandit approach"):
  with members:
    - "delay and cooperation in nonstochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "a stable marriage requires communication"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "almost stable matchings by truncating the gale shapley algorithm"

Connected through hyperedge HE-6 (induced by source paper "joint doa and frequency estimation with sub nyquist sampling in the sparse array system"):
  with members:
    - "joint doa and frequency estimation with sub nyquist sampling"

Connected through hyperedge HE-7 (induced by source paper "compressed wideband spectrum sensing concept challenges and enablers"):
  with members:
    - "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"

Connected through hyperedge HE-8 (induced by source paper "non uniform wavelet sampling for rf analog to information conversion"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "stable and robust sampling strategies for compressive imaging"
    - "structured compressed sensing from theory to applications"
    - "noise folding in compressed sensing"
    - "recovery of sparsely corrupted signals"
    - "xampling signal acquisition and processing in union of subspaces"
    - "sub nyquist sampling of short pulses"

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)