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: "asymptotic sum capacity of random gaussian interference networks using interference alignment"
  Abstract: We consider a dense n-user Gaussian interference network formed by paired transmitters and receivers placed independently at random in Euclidean space. Under natural conditions on the node position distributions and signal attenuation, we prove convergence in probability of the average per-user capacity C_Sigma/n to 1/2 E log(1 + 2SNR). #R##N#The achievability result follows directly from results based on an interference alignment scheme presented in recent work of Nazer et al. Our main contribution comes through the converse result, motivated by ideas of `bottleneck links' developed in recent work of Jafar. An information theoretic argument gives a capacity bound on such bottleneck links, and probabilistic counting arguments show there are sufficiently many such links to tightly bound the sum-capacity of the whole network.

Connected through hyperedge HE-1 (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"
    - "delay rate tradeoff for ergodic interference alignment in the gaussian case"
    - "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-2 (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"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"

Connected through hyperedge HE-3 (induced by source paper "the ergodic capacity of phase fading interference networks"):
  with members:
    - "interference alignment based sum capacity bounds for random dense gaussian interference networks"
    - "ergodic interference alignment"
    - "multiple access outerbounds and the inseparability of parallel interference channels"
    - "generalized degrees of freedom of the symmetric gaussian k user interference channel"
    - "gaussian interference channel capacity to within one bit"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-4 (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"
    - "ergodic interference alignment"
    - "throughput delay trade off for hierarchical cooperation in ad hoc wireless networks"
    - "gaussian interference channel capacity to within one bit"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-5 (induced by source paper "the feasibility of interference alignment over measured mimo ofdm channels"):
  with members:
    - "cooperative algorithms for mimo interference channels"
    - "interference alignment with limited feedback"
    - "on degrees of freedom region of mimo networks without csit"
    - "on the achievability of interference alignment in the k user constant mimo interference channel"
    - "approaching the capacity of wireless networks through distributed interference alignment"

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)