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: "rate splitting in the presence of multiple receivers"
  Abstract: In the presence of multiple senders, one of the simplest decoding strategies that can be employed by a receiver is successive decoding. In a successive decoding strategy, the receiver decodes the messages one at a time using the knowledge of the previously decoded messages as side information. Recently, there have been two separate attempts to construct codes for the interference channel using successive decoding based on the idea of rate-splitting. #R##N#In this note, we highlight a difficulty that arises when a rate-splitting codebook is to be decoded by multiple receivers. The main issue is that the rates of the split codebook are tightly coupled to the properties of the channel to the receiver, thus, rates chosen for one of the receivers may not be decodable for the other. We illustrate this issue by scrutinizing two recent arguments claiming to achieve the Han-Kobayashi rate region for the interference channel using rate-splitting and successive decoding.

Connected through hyperedge HE-1 (induced by source paper "sliding window superposition coding two user interference channels"):
  with members:
    - "optimal achievable rates for interference networks with random codes"
    - "polar coding for interference networks"
    - "is non unique decoding necessary"
    - "a comparison of superposition coding schemes"
    - "universal codes for the gaussian mac via spatial coupling"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "to decode the interference or to consider it as noise"

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)