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: "delay rate tradeoff for ergodic interference alignment in the gaussian case"
  Abstract: In interference alignment, users sharing a wireless channel are each able to achieve data rates of up to half of the non-interfering channel capacity, no matter the number of users. In an ergodic setting, this is achieved by pairing complementary channel realizations in order to amplify signals and cancel interference. However, this scheme has the possibility for large delays in decoding message symbols. We show that delay can be mitigated by using outputs from potentially more than two channel realizations, although data rate may be reduced. We further demonstrate the tradeoff between rate and delay via a time-sharing strategy. Our analysis considers Gaussian channels; an extension to finite field channels is also possible.

Connected through hyperedge HE-1 (induced by source paper "channel aided interference alignment"):
  with members:
    - "the feasibility conditions for interference alignment in mimo networks"
    - "opportunistic interference alignment for mimo interfering multiple access channels"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "ergodic fading interference channels sum capacity and separability"
    - "degrees of freedom region for an interference network with general message demands"
    - "ergodic interference alignment"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"

Connected through hyperedge HE-2 (induced by source paper "delay rate tradeoff in ergodic interference alignment"):
  with members:
    - "the ergodic capacity of phase fading interference networks"
    - "interference alignment based sum capacity bounds for random dense gaussian interference networks"
    - "asymptotic sum capacity of random gaussian interference networks using interference alignment"
    - "the feasibility of interference alignment over measured mimo ofdm channels"
    - "ergodic interference alignment"
    - "interference alignment for line of sight channels"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-3 (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"
    - "asymptotic sum capacity of random gaussian interference networks using interference alignment"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"

Connected through hyperedge HE-4 (induced by source paper "ergodic interference alignment"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "on the degrees of freedom achievable through interference alignment in a mimo interference channel"
    - "delay rate tradeoff in ergodic interference alignment"
    - "the ergodic capacity of phase fading interference networks"
    - "settling the feasibility of interference alignment for the mimo interference channel the symmetric square case"
    - "interference alignment based sum capacity bounds for random dense gaussian interference networks"
    - "ergodic fading interference channels sum capacity and separability"

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)