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: "log concavity property of the error probability with application to local bounds for wireless communications"
  Abstract: A clear understanding of the behavior of error probability (EP) as a function of signal-to-noise ratio (SNR) and other system parameters is fundamental for assessing the design of digital wireless communication systems. We propose an analytical framework based on the log-concavity property of the EP which we prove for a wide family of multidimensional modulation formats in the presence of Gaussian disturbances and fading. Based on this property, we construct a class of local bounds for the EP that improve known generic bounds in a given region of the SNR and are invertible, as well as easily tractable for further analysis. This concept is motivated by the fact that communication systems often operate with performance in a certain region of interest (ROI) and, thus, it may be advantageous to have tighter bounds within this region instead of generic bounds valid for all SNRs. We present a possible application of these local bounds, but their relevance is beyond the example made in this paper.

Connected through hyperedge HE-1 (induced by source paper "on convexity of error rates in digital communications"):
  with members:
    - "error rates of capacity achieving codes are convex"
    - "error rates of the maximum likelihood detector for arbitrary constellations convex concave behavior and applications"
    - "bit error rate is convex at high snr"
    - "on node density outage probability tradeoff in wireless networks"
    - "on optimum power allocation for the v blast"
    - "symbol error rates of maximum likelihood detector convex concave behavior and applications"

Connected through hyperedge HE-2 (induced by source paper "secure diversity multiplexing tradeoff of zero forcing transmit scheme at finite snr"):
  with members:
    - "error rates of the maximum likelihood detector for arbitrary constellations convex concave behavior and applications"
    - "secure transmission with multiple antennas ii the mimome wiretap channel"
    - "secure communication in the low snr regime a characterization of the energy secrecy tradeoff"
    - "the secrecy capacity region of the gaussian mimo multi receiver wiretap channel"
    - "on the throughput of secure hybrid arq protocols for gaussian block fading channels"
    - "diversity multiplexing tradeoff for the multiple antenna wire tap channel"
    - "impact of spatial correlation on the finite snr diversity multiplexing tradeoff"
    - "the secrecy capacity of the mimo wiretap channel"

Connected through hyperedge HE-3 (induced by source paper "error rates of the maximum likelihood detector for arbitrary constellations convex concave behavior and applications"):
  with members:
    - "optimum power and rate allocation for coded v blast"
    - "on optimum power allocation for the v blast"

Connected through hyperedge HE-4 (induced by source paper "bit error rate is convex at high snr"):
  with members:
    - "error rates of the maximum likelihood detector for arbitrary constellations convex concave behavior and applications"
    - "on optimum power allocation for the v blast"

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)