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: "on optimum power allocation for the v blast"
  Abstract: A unified analytical framework for optimum power allocation in the unordered V-BLAST algorithm and its comparative performance analysis are presented. Compact closed-form approximations for the optimum power allocation are derived, based on average total and block error rates. The choice of the criterion has little impact on the power allocation and, overall, the optimum strategy is to allocate more power to lower step transmitters and less to higher ones. High-SNR approximations for optimized average block and total error rates are given. The SNR gain of optimization is rigorously defined and studied using analytical tools, including lower and upper bounds, high and low SNR approximations. The gain is upper bounded by the number of transmit antennas, for any modulation format and type of fading channel. While the average optimization is less complex than the instantaneous one, its performance is almost as good at high SNR. A measure of robustness of the optimized algorithm is introduced and evaluated. The optimized algorithm is shown to be robust to perturbations in individual and total transmit powers. Based on the algorithm robustness, a pre-set power allocation is suggested as a low-complexity alternative to the other optimization strategies, which exhibits only a minor loss in performance over the practical SNR range.

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"
    - "log concavity property of the error probability with application to local bounds for wireless communications"
    - "symbol error rates of maximum likelihood detector convex concave behavior and applications"

Connected through hyperedge HE-2 (induced by source paper "optimum power and rate allocation for coded v blast average optimization"):
  with members:
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "on outage and error rate analysis of the ordered v blast"

Connected through hyperedge HE-3 (induced by source paper "error rates of capacity achieving codes are convex"):
  with members:
    - "error rates of the maximum likelihood detector for arbitrary constellations convex concave behavior and applications"
    - "symbol error rates of maximum likelihood detector convex concave behavior and applications"

Connected through hyperedge HE-4 (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"
    - "log concavity property of the error probability with application to local bounds for wireless communications"

Connected through hyperedge HE-5 (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"
    - "log concavity property of the error probability with application to local bounds for wireless communications"

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)