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: "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"
  Abstract: In multi-user information theory it is often assumed that every node in the network possesses all codebooks used in the network. This assumption is however impractical in distributed ad-hoc and cognitive networks. This work considers the two- user Gaussian Interference Channel with one Oblivious Receiver (G-IC-OR), i.e., one receiver lacks knowledge of the interfering cookbook while the other receiver knows both codebooks. We ask whether, and if so how much, the channel capacity of the G-IC- OR is reduced compared to that of the classical G-IC where both receivers know all codebooks. Intuitively, the oblivious receiver should not be able to jointly decode its intended message along with the unintended interfering message whose codebook is unavailable. We demonstrate that in strong and very strong interference, where joint decoding is capacity achieving for the classical G-IC, lack of codebook knowledge does not reduce performance in terms of generalized degrees of freedom (gDoF). Moreover, we show that the sum-capacity of the symmetric G-IC- OR is to within O(log(log(SNR))) of that of the classical G-IC. The key novelty of the proposed achievable scheme is the use of a discrete input alphabet for the non-oblivious transmitter, whose cardinality is appropriately chosen as a function of SNR.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of cloud radio access networks with oblivious relaying"):
  with members:
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "opening the black box of deep neural networks via information"
    - "lossy compression for compute and forward in limited backhaul uplink multicell processing"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "deep learning and the information bottleneck principle"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"

Connected through hyperedge HE-2 (induced by source paper "interference as noise friend or foe"):
  with members:
    - "on the two user interference channel with lack of knowledge of the interference codebook at one receiver"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"
    - "approximate sum capacity of k user cognitive interference channels with cumulative message sharing"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "wireless network information flow a deterministic approach"
    - "interference alignment from degrees of freedom to constant gap capacity approximations"
    - "k user interference channels general outer bound and sum capacity for certain gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "the effect of maximal rate codes on the interfering message rate"):
  with members:
    - "on mmse properties of codes for the gaussian broadcast and wiretap channels"
    - "sub optimality of the han kobayashi achievable region for interference channels"
    - "random coding error exponents for the two user interference channel"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "on the two user interference channel with lack of knowledge of the interference codebook at one receiver"
    - "sub optimality of treating interference as noise in the cellular uplink with weak interference"
    - "on the optimality of treating interference as noise general message sets"
    - "is non unique decoding necessary"

Connected through hyperedge HE-4 (induced by source paper "on the two user interference channel with lack of knowledge of the interference codebook at one receiver"):
  with members:
    - "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"
    - "wireless network information flow a deterministic approach"
    - "a coordinate system for gaussian networks"
    - "the two user gaussian interference channel a deterministic view"
    - "gaussian interference channel capacity to within one bit"

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)