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: "partial decode forward binning schemes for the causal cognitive relay channels"
  Abstract: The causal cognitive relay channel (CRC) has two sender-receiver pairs, in which the second sender obtains information from the first sender causally and assists the transmission of both senders. In this paper, we study both the full- and half-duplex modes. In each mode, we propose two new coding schemes built successively upon one another to illustrate the impact of different coding techniques. The first scheme called partial decode-forward binning (PDF-binning) combines the ideas of partial decode-forward relaying and Gelfand-Pinsker binning. The second scheme called Han-Kobayashi partial decode-forward binning (HK-PDF-binning) combines PDF-binning with Han-Kobayashi coding by further splitting rates and applying superposition coding, conditional binning and relaxed joint decoding. #R##N#In both schemes, the second sender decodes a part of the message from the first sender, then uses Gelfand-Pinsker binning technique to bin against the decoded codeword, but in such a way that allows both state nullifying and forwarding. For the Gaussian channels, this PDF-binning essentializes to a correlation between the transmit signal and the binning state, which encompasses the traditional dirty-paper-coding binning as a special case when this correlation factor is zero. We also provide the closed-form optimal binning parameter for each scheme. #R##N#The 2-phase half-duplex schemes are adapted from the full-duplex ones by removing block Markov encoding, sending different message parts in different phases and applying joint decoding across both phases. Analysis shows that the HK-PDF-binning scheme in both modes encompasses the Han-Kobayashi rate region and achieves both the partial decode-forward relaying rate for the first sender and interference-free rate for the second sender. Furthermore, this scheme outperforms all existing schemes.

Connected through hyperedge HE-1 (induced by source paper "interference channels with half duplex source cooperation"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "gaussian z interference channel with a relay link achievability region and asymptotic sum capacity"
    - "capacity bounds of half duplex gaussian cooperative interference channel"
    - "interference mitigation through limited receiver cooperation"
    - "capacity bounds for two hop interference networks"
    - "the two user gaussian interference channel a deterministic view"

Connected through hyperedge HE-2 (induced by source paper "on the capacity of the two user gaussian causal cognitive interference channel"):
  with members:
    - "an outer bound for the memoryless two user interference channel with general cooperation"
    - "on the capacity of interference channel with causal and noncausal generalized feedback at the cognitive transmitter"
    - "experiment driven characterization of full duplex wireless systems"
    - "inner and outer bounds for the gaussian cognitive interference channel and new capacity results"
    - "dependence balance based outer bounds for gaussian networks with cooperation and feedback"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "feedback capacity of the gaussian interference channel to within 2 bits"

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)