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: "efficient capacity computation and power optimization for relay networks"
  Abstract: The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. #R##N#For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power optimization problems and show that they can be solved in polynomial time. In particular, we show that the minimization of the sum of powers assigned to the nodes subject to a minimum rate constraint (measured in terms of cut-set bounds) can be computed in polynomial time. We propose an heuristic algorithm to solve this problem and measure its performance through simulations on random Gaussian networks. We observe that in the optimal allocations most of the power is assigned to a small subset of relays, which suggests that network simplification may be possible without excessive performance degradation.

Connected through hyperedge HE-1 (induced by source paper "polynomial time capacity calculation and scheduling for half duplex 1 2 1 networks"):
  with members:
    - "gaussian 1 2 1 networks capacity results for mmwave communications"
    - "efficiently finding simple schedules in gaussian half duplex relay line networks"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"

Connected through hyperedge HE-2 (induced by source paper "on the optimality of simple schedules for networks with multiple half duplex relays"):
  with members:
    - "cut set bound is loose for gaussian relay networks"
    - "on the gaussian half duplex relay channel"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "capacity of gaussian channels with duty cycle and power constraints"
    - "on capacity and optimal scheduling for the half duplex multiple relay channel"
    - "wireless network information flow a deterministic approach"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-3 (induced by source paper "the approximate optimality of simple schedules for half duplex multi relay networks"):
  with members:
    - "on the optimality of simple schedules for networks with multiple half duplex relays"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "on capacity and optimal scheduling for the half duplex multiple relay channel"
    - "learning with submodular functions a convex optimization perspective"
    - "on the capacity of the half duplex diamond channel"

Connected through hyperedge HE-4 (induced by source paper "on information flow and feedback in relay networks"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "improved capacity approximations for gaussian relay networks"
    - "wireless network information flow a deterministic approach"
    - "wireless network simplification the gaussian n relay diamond network"
    - "approximately optimal wireless broadcasting"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "a theory of network equivalence parts i and ii"
    - "approximately achieving gaussian relay network capacity with lattice codes"

Connected through hyperedge HE-5 (induced by source paper "wireless network simplification the gaussian n relay diamond network"):
  with members:
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "noisy network coding"
    - "approximately achieving gaussian relay network capacity with lattice codes"
    - "a simple cooperative diversity method based on network path selection"

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)