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: "capacity of wireless networks within o log snr the impact of relays feedback cooperation and full duplex operation"
  Abstract: Recent work has characterized the sum capacity of time-varying/frequency-selective wireless interference networks and $X$ networks within $o(\log({SNR}))$, i.e., with an accuracy approaching 100% at high SNR (signal to noise power ratio). In this paper, we seek similar capacity characterizations for wireless networks with relays, feedback, full duplex operation, and transmitter/receiver cooperation through noisy channels. First, we consider a network with $S$ source nodes, $R$ relay nodes and $D$ destination nodes with random time-varying/frequency-selective channel coefficients and global channel knowledge at all nodes. We allow full-duplex operation at all nodes, as well as causal noise-free feedback of all received signals to all source and relay nodes. The sum capacity of this network is characterized as $\frac{SD}{S+D-1}\log({SNR})+o(\log({SNR}))$. The implication of the result is that the capacity benefits of relays, causal feedback, transmitter/receiver cooperation through physical channels and full duplex operation become a negligible fraction of the network capacity at high SNR. Some exceptions to this result are also pointed out in the paper. Second, we consider a network with $K$ full duplex nodes with an independent message from every node to every other node in the network. We find that the sum capacity of this network is bounded below by $\frac{K(K-1)}{2K-2}+o(\log({SNR}))$ and bounded above by $\frac{K(K-1)}{2K-3}+o(\log({SNR}))$.

Connected through hyperedge HE-1 (induced by source paper "a layered lattice coding scheme for a class of three user gaussian interference channels"):
  with members:
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "gaussian interference networks sum capacity in the low interference regime and new outer bounds on the capacity region"
    - "capacity bounds for the gaussian interference channel"
    - "capacity of symmetric k user gaussian very strong interference channels"
    - "the case for structured random codes in network capacity theorems"
    - "lattices for distributed source coding jointly gaussian sources and reconstruction of a linear function"
    - "interference alignment on the deterministic channel and application to fully connected awgn interference networks"

Connected through hyperedge HE-2 (induced by source paper "on the capacity and generalized degrees of freedom of the x channel"):
  with members:
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "gaussian interference networks sum capacity in the low interference regime and new outer bounds on the capacity region"
    - "capacity bounds for the gaussian interference channel"
    - "the two user gaussian interference channel a deterministic view"
    - "generalized degrees of freedom of the symmetric gaussian k user interference channel"
    - "a deterministic approach to wireless relay networks"
    - "interference alignment on the deterministic channel and application to fully connected awgn interference networks"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-3 (induced by source paper "degrees of freedom of the mimo interference channel with cooperation and cognition"):
  with members:
    - "on the capacity of a class of mimo cognitive radios"
    - "on cognitive interference networks"
    - "the multiplexing gain of mimo x channels with partial transmit side information"
    - "degrees of freedom region for the mimo x channel"
    - "cognitive radio an information theoretic perspective"

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)