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: "throughput and collision analysis of multichannel multistage spectrum sensing algorithms"
  Abstract: Multistage sensing is a novel concept that refers to a general class of spectrum sensing algorithms that divide the sensing process into a number of sequential stages. The number of sensing stages and the sensing technique per stage can be used to optimize the performance with respect to secondary user (SU) throughput and the collision probability between primary user (PU) and SU. So far, the impact of multistage sensing on network throughput and collision probability for a realistic network model has been relatively unexplored. Therefore, we present the first analytical framework that enables the performance evaluation of different multichannel multistage spectrum sensing algorithms for opportunistic spectrum access (OSA) networks. The contribution of this paper lies in studying the effect of the following parameters on performance: number of sensing stages, physical layer sensing techniques and durations per each stage, single and parallel channel sensing and access, number of available channels, PU and SU traffic, buffering of incoming SU traffic, and medium access control layer sensing algorithms. The analyzed performance metrics include the average SU throughput and the average collision probability between PU and SU. Our results show that when the probability of PU misdetection is constrained, the performance of multistage sensing is, in most cases, superior to the single-stage sensing counterpart. In addition, prolonged channel observation at the first stage of sensing considerably decreases the collision probability while keeping the throughput at an acceptable level. Finally, in realistic PU traffic scenarios, using two stages of sensing provides a good balance between SU throughput and collision probability while meeting successful detection constraints subjected by OSA communication.

Connected through hyperedge HE-1 (induced by source paper "multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test"):
  with members:
    - "eigenvalue based cyclostationary spectrum sensing using multiple antennas"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-2 (induced by source paper "primary user traffic estimation for dynamic spectrum access"):
  with members:
    - "a cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks"
    - "exploiting channel correlation and pu traffic memory for opportunistic spectrum scheduling"
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "sensitive white space detection with spectral covariance sensing"

Connected through hyperedge HE-3 (induced by source paper "distributed multiuser sequential channel sensing schemes in multichannel cognitive radio networks"):
  with members:
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "sensing matrix setting schemes for cognitive networks and their performance analysis"
    - "optimal medium access control in cognitive radios a sequential design approach"
    - "cognitive medium access exploration exploitation and competition"

Connected through hyperedge HE-4 (induced by source paper "analysis framework for opportunistic spectrum ofdma and its application to the ieee 802 22 standard"):
  with members:
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

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.PF(Performance)