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 the two user interference channel with lack of knowledge of the interference codebook at one receiver"
  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 may be impractical in distributed ad-hoc, cognitive or heterogeneous networks. This work considers the two-user Interference Channel with one Oblivious Receiver (IC-OR), i.e., one receiver lacks knowledge of the interfering cookbook while the other receiver knows both codebooks. The paper asks whether, and if so how much, the channel capacity of the IC-OR is reduced compared to that of the classical IC where both receivers know all codebooks. A novel outer bound is derived and shown to be achievable to within a gap for the class of injective semi-deterministic IC-ORs; the gap is shown to be zero for injective fully deterministic IC-ORs. For the linear deterministic IC-OR that models the Gaussian noise channel at high SNR, non i.i.d. Bernoulli(1/2) input bits are shown to achieve points not achievable by i.i.d. Bernoulli(1/2) input bits used in the same achievability scheme. For the real-valued Gaussian IC-OR the gap is shown to be at most 1/2 bit per channel use, even though the set of optimal input distributions for the derived outer bound could not be determined. Towards understanding the Gaussian IC-OR, an achievability strategy is evaluated in which the input alphabets at the non-oblivious transmitter are a mixture of discrete and Gaussian random variables, where the cardinality of the discrete part is appropriately chosen as a function of the channel parameters. Surprisingly, as the oblivious receiver intuitively should not be able to 'jointly decode' the intended and interfering messages (whose codebook is unavailable), it is shown that with this choice of input, the capacity region of the symmetric Gaussian IC-OR is to within 3.34 bits (per channel use per user) of an outer bound for the classical Gaussian IC with full codebook knowledge.

Connected through hyperedge HE-1 (induced by source paper "uplink non orthogonal multiple access with finite alphabet inputs"):
  with members:
    - "on non orthogonal multiple access with finite alphabet inputs in z channels"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "capacity comparison between mimo noma and mimo oma with multiple users in a cluster"
    - "joint rate control and power allocation for non orthogonal multiple access systems"
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "relay selection for cooperative noma"
    - "quadrature amplitude modulation division for multiuser miso broadcast channels"
    - "non orthogonal multiple access in multi cell networks theory performance and practical challenges"

Connected through hyperedge HE-2 (induced by source paper "on communication through a gaussian channel with an mmse disturbance constraint"):
  with members:
    - "interference as noise friend or foe"
    - "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"
    - "analysis of mismatched estimation errors using gradients of partition functions"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"

Connected through hyperedge HE-3 (induced by source paper "interference as noise friend or foe"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"
    - "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-4 (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"
    - "sub optimality of treating interference as noise in the cellular uplink with weak interference"
    - "on discrete alphabets for the two user gaussian interference channel with one receiver lacking knowledge of the interfering codebook"
    - "on the optimality of treating interference as noise general message sets"
    - "is non unique decoding necessary"

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)