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 in ergodic interference alignment"
  Abstract: Ergodic interference alignment, as introduced by Nazer et al (NGJV), is a technique that allows high-rate communication in n-user interference networks with fast fading. It works by splitting communication across a pair of fading matrices. However, it comes with the overhead of a long time delay until matchable matrices occur: the delay is qn2 for field size q. In this paper, we outline two new families of schemes, called JAP and JAP-B, that reduce the expected delay, sometimes at the cost of a reduction in rate from the NGJV scheme. In particular, we give examples of good schemes for networks with few users, and show that in large n-user networks, the delay scales like qT, where T is quadratic in n for a constant per-user rate and T is constant for a constant sum-rate. We also show that half the single-user rate can be achieved while reducing NGJV's delay from qn2 to q(n-1)(n-2).

Connected through hyperedge HE-1 (induced by source paper "asymptotic sum capacity of random gaussian interference networks using interference alignment"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "interference alignment based sum capacity bounds for random dense gaussian interference networks"
    - "the feasibility of interference alignment over measured mimo ofdm channels"
    - "ergodic interference alignment"

Connected through hyperedge HE-2 (induced by source paper "delay rate tradeoff for ergodic interference alignment in the gaussian case"):
  with members:
    - "ergodic interference alignment"
    - "multiple access outerbounds and the inseparability of parallel interference channels"

Connected through hyperedge HE-3 (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"
    - "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"
    - "interference alignment for the mimo interference channel with delayed local csit"

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)