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: "cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices"
  Abstract: Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. These techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far are based on asymptotical assumptions, due to the difficulties in characterizing the exact eigenvalues ratio distribution. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then applied to calculate the decision sensing threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial improvement compared to the other eigenvalue-based algorithms.

Connected through hyperedge HE-1 (induced by source paper "decentralized eigenvalue algorithms for distributed signal detection in cognitive networks"):
  with members:
    - "spectrum sensing in the presence of multiple primary users"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"
    - "distributed consensus over wireless sensor networks affected by multipath fading"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-2 (induced by source paper "statistical inference in large antenna arrays under unknown noise pattern"):
  with members:
    - "eigen inference for energy estimation of multiple sources"
    - "cooperative spectrum sensing using random matrix theory"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"

Connected through hyperedge HE-3 (induced by source paper "eigenvalue based sensing and snr estimation for cognitive radio in presence of noise correlation"):
  with members:
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "cooperative spectrum sensing using random matrix theory"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"
    - "spectrum sensing algorithms for cognitive radio based on statistical covariances"

Connected through hyperedge HE-4 (induced by source paper "locally best invariant test for multiple primary user spectrum sensing"):
  with members:
    - "spectrum sensing in the presence of multiple primary users"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-5 (induced by source paper "spectrum sensing in the presence of multiple primary users"):
  with members:
    - "theoretical performance analysis of eigenvalue based detection"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-6 (induced by source paper "on the performance of spectrum sensing algorithms using multiple antennas"):
  with members:
    - "cooperative spectrum sensing using random matrix theory"
    - "eigenvalue based spectrum sensing algorithms for cognitive 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)