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 "context aware decentralized invariant signaling for opportunistic communications"):
  Abstract: A novel scenario-adapted distributed signaling technique in the context of opportunistic communications is presented in this work. Each opportunistic user acquires locally sampled observations from the wireless environment to determine the occupied and available degrees-of-freedom (DoF). Due to sensing errors and locality of observations, a performance loss and inter-system interference arise from subspace uncertainties. Yet, we show that addressing the problem as a total least-squares (TLS) optimization, signaling patterns robust to subspace uncertainties can be designed. Furthermore, given the equivalence of minimum norm and TLS, the latter exhibits the interesting properties of linear predictors. Specifically, the rotationally invariance property is of paramount importance to guarantee the detectability by neighboring nodes. Albeit these advantages, end-to-end subspace uncertainties yield a performance loss that compromises both detectability and wireless environment's performance. To combat the latter, we tackle the distributed identification of the active subspace with and without side information about neighboring nodes' subspaces. An extensive simulation analysis highlights the performance of distributed concurrency schemes to achieve subspace agreement.

Includes members:
  Member "state of the art taxonomy and open issues on cognitive radio networks with noma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of rate optimal power domain noma schemes for enabling technologies of future wireless networks")
  Member "resource allocation and fairness in wireless powered cooperative cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online policies for throughput maximization of energy constrained wireless powered communication systems")
  Member "covariance based ofdm spectrum sensing with sub nyquist samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive detection of random subspace signals")
  Member "vandermonde subspace frequency division multiplexing for two tiered cognitive radio networks":
    also connected through other hyperedges:
      (none)
  Member "distributed optimal beamformers for cognitive radios robust to channel uncertainties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel and distributed methods for nonconvex optimization part ii applications")
  Member "enhanced intercell interference coordination challenges in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local partial zero forcing precoding for cell free massive mimo")
  Member "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal precoder designs for sum utility maximization in swipt enabled multi user mimo cognitive radio networks")

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)