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: "throughput delay trade off for hierarchical cooperation in ad hoc wireless networks"
  Abstract: Hierarchical cooperation has recently been shown to achieve better throughput scaling than classical multihop schemes in static wireless networks. However, the end-to-end delay of this scheme turns out to be significantly larger than those of multihop schemes. A modification of the scheme is proposed here that achieves a throughput-delay trade-off D(n) = (logn)2T(n) for T(n) between thetas(radicn/ log n) and thetas(n/ log n), where D(n) and T(n) are respectively the average delay per bit and the aggregate throughput in a network of n nodes. This trade-off complements the previous results, which show that the throughput-delay trade-off for multihop schemes is given by D(n) = T(n) where T(n) lies between thetas(1) and thetas(radicn).

Connected through hyperedge HE-1 (induced by source paper "interference mitigation in large random wireless networks"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "boolean compressed sensing and noisy group testing"
    - "wireless network information flow a deterministic approach"
    - "interference alignment based sum capacity bounds for random dense gaussian interference networks"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "neighbor discovery for wireless networks via compressed sensing"
    - "delay rate tradeoff for ergodic interference alignment in the gaussian case"
    - "asymptotic sum capacity of random gaussian interference networks using interference alignment"

Connected through hyperedge HE-2 (induced by source paper "interference alignment based sum capacity bounds for random dense gaussian interference networks"):
  with members:
    - "the ergodic capacity of phase fading interference networks"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "asymptotic sum capacity of random gaussian interference networks using interference alignment"
    - "ergodic interference alignment"
    - "gaussian interference channel capacity to within one bit"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-3 (induced by source paper "scaling laws for overlaid wireless networks a cognitive radio network vs a primary network"):
  with members:
    - "cognitive networks achieve throughput scaling of a homogeneous network"
    - "scaling laws of cognitive networks"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

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)