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: "gelfand pinsker coding achieves the interference free capacity"
  Abstract: For a discrete memoryless channel with non-causal state information available only at the encoder, it is well-known that Gelfand-Pinsker coding achieves its capacity. In this paper, we analyze Gelfand-Pinsker coding scheme and capacity to bring out further understandings. We show that Gelfand-Pinsker capacity is equal to the interference-free capacity. Thus the capacity of a channel with non-causal state information available only at the encoder is the same as if the state information is also available at the decoder. Furthermore, the capacity-achieving conditional input distributions in these two cases are the same. This lets us connect the studied channel with state to the multiple access channel (MAC) with correlated sources and show that under certain conditions, the receiver can decode both the message and the state information. This dual decoding can be obtained in particular if the state sequences come from a known codebook with rate satisfying a simple constraint. In such a case, we can modify Gelfand-Pinsker coding by pre-building multiple codebooks of input sequences $X^n$, each codebook is for a given state sequence $S^n$, upon generating the auxiliary $U^n$ sequences. The modified Gelfand-Pinsker coding scheme achieves the capacity of the MAC with degraded message set and still allows for decoding of just the message at any state information rate. We then revisit dirty-paper coding for the Gaussian channel to verify our analysis and modified coding scheme.

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)