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 the optimality of simple schedules for networks with multiple half duplex relays"
  Abstract: This paper studies networks that consist of    $N$    half-duplex relays assisting the communication between a source and a destination. In ISIT’12 Brahma  et al.  conjectured that in Gaussian half-duplex diamond networks (i.e., without a direct link between the source and the destination, and with    $N$    non-interfering relays), an approximately optimal relay scheduling policy (i.e., achieving the cut-set upper bound to within a constant gap uniformly over all channel gains) has at most    $N+1$    active states (i.e., at most    $N+1$    out of the    $2^{N}$    possible relay listen-transmit configurations have a strictly positive probability). Such relay scheduling policies were referred to as simple. In ITW’13, we conjectured that simple approximately optimal relay scheduling policies exist for any Gaussian half-duplex multi-relay network irrespectively of the topology. This paper formally proves this more general version of the conjecture and shows it holds beyond Gaussian noise networks. In particular, for any class of memoryless half-duplex    $N$   -relay networks with independent noises and for which independent inputs are approximately optimal in the cut-set upper bound, an approximately optimal simple relay scheduling policy exists. The key step of the proof is to write the minimum of the submodular cut-set function by means of its Lovasz extension and use the greedy algorithm for submodular polyhedra to highlight structural properties of the optimal solution. This, together with the saddle-point property of min–max problems and the existence of optimal basic feasible solutions for linear programs, proves the conjecture. As an example, for    $N$   -relay Gaussian networks with independent noises, where each node is equipped with multiple antennas and where each antenna can be configured to listen or transmit irrespectively of the others, the existence of an approximately optimal simple relay scheduling policy with at most    $N+1$    active states, irrespectively of the total number of antennas in the system, is proved.

Connected through hyperedge HE-1 (induced by source paper "best relay selection in gaussian half duplex diamond networks"):
  with members:
    - "efficiently finding simple schedules in gaussian half duplex relay line networks"
    - "softnull many antenna full duplex wireless via digital beamforming"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "wireless network simplification beyond diamond networks"
    - "design and characterization of a full duplex multiantenna system for wifi networks"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-2 (induced by source paper "the approximate optimality of simple schedules for half duplex multi relay networks"):
  with members:
    - "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"
    - "efficient capacity computation and power optimization for relay networks"
    - "learning with submodular functions a convex optimization perspective"
    - "on the capacity of the half duplex diamond channel"

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)