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: "interference alignment based sum capacity bounds for random dense gaussian interference networks"
  Abstract: We consider a dense K user Gaussian interference network formed by paired transmitters and receivers placed independently at random in a fixed spatial region. Under natural conditions on the node position distributions and signal attenuation, we prove convergence in probability of the average per-user capacity CΣ/K to 1/2E log(1 + 2SNR). 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 an upper bound, motivated by ideas of "bottleneck capacity" developed in recent work of Jafar. By controlling the physical location of transmitter-receiver pairs, we can match a large proportion of these pairs to form so-called e-bottleneck links, with consequent control of the sum capacity.

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"
    - "delay rate tradeoff for ergodic interference alignment in the gaussian case"
    - "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-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"
    - "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"
    - "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:
    - "asymptotic sum capacity of random gaussian interference networks using interference alignment"
    - "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 "performance of interference alignment in clustered wireless ad hoc networks"):
  with members:
    - "on feasibility of interference alignment in mimo interference networks"
    - "on feasibility of interference alignment in mimo interference networks"
    - "on the achievability of interference alignment in the k user constant mimo interference channel"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "interference alignment with asymmetric complex signaling settling the host madsen nosratinia conjecture"
    - "interference and outage in clustered wireless ad hoc networks"
    - "degrees of freedom of the k user m times n mimo interference channel"
    - "approaching the capacity of wireless networks through distributed interference alignment"

Connected through hyperedge HE-5 (induced by source paper "asymptotic sum capacity of random gaussian interference networks using interference alignment"):
  with members:
    - "delay rate tradeoff in ergodic interference alignment"
    - "wireless network information flow a deterministic approach"
    - "the feasibility of interference alignment over measured mimo ofdm channels"
    - "ergodic interference alignment"

Connected through hyperedge HE-6 (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"
    - "ergodic fading interference channels sum capacity and separability"
    - "interference alignment for the mimo interference channel with delayed local csit"

Connected through hyperedge HE-7 (induced by source paper "interference alignment in dense wireless networks"):
  with members:
    - "the ergodic capacity of phase fading interference networks"
    - "the balanced unicast and multicast capacity regions of large wireless networks"
    - "on capacity scaling in arbitrary wireless networks"
    - "ergodic interference alignment"
    - "approaching the capacity of wireless networks through distributed interference alignment"
    - "product multicommodity flow in wireless networks"
    - "information theoretic operating regimes of large wireless networks"
    - "on information theoretic scaling laws for wireless 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)