human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "joint optimization of spectrum sensing and accessing in multiuser miso cognitive networks"):
  Abstract: In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi-antenna technique, such as zero-forcing beamforming (ZFBF), is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the above two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi-antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.

Includes members:
  Member "energy efficient joint source and channel sensing in cognitive radio sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficient power allocation for secure communications in large scale mimo relaying systems")
  Member "interference aware resource control in multi antenna cognitive ad hoc networks with heterogeneous delay constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on interference alignment with imperfect csi characterizations of outage probability ergodic rate and ser")
  Member "adaptive mode selection in multiuser miso cognitive networks with limited cooperation and feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "feedback bits allocation for interference minimization in cognitive radio communications")
  Member "multi mode transmission for the mimo broadcast channel with imperfect channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provisioning energy efficiency and qos for multi carrier comp with limited feedback")
  Member "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal precoder designs for sum utility maximization in swipt enabled multi user mimo cognitive radio networks")
  Member "eigenvalue based spectrum sensing algorithms for cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "mimo broadcast channels with finite rate feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")

Question: Please tell me which class the source paper 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)