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: "interference channels with half duplex source cooperation"
  Abstract: The performance gain by allowing half-duplex source cooperation is studied for Gaussian interference channels. The source cooperation is {\em in-band}, meaning that each source can listen to the other source's transmission, but there is no independent (or orthogonal) channel between the sources. The half-duplex constraint supposes that at each time instant the sources can either transmit or listen, but not do both. #R##N#Our main result is a characterization of the sum capacity when the cooperation is bidirectional and the channel gains are symmetric. With unidirectional cooperation, we essentially have a cognitive radio channel. By requiring the primary to achieve a rate close to its link capacity, the best possible rate for the secondary is characterized within a constant. Novel inner and outer bounds are derived as part of these characterizations.

Connected through hyperedge HE-1 (induced by source paper "cellular interference alignment"):
  with members:
    - "cellular interference alignment omni directional antennas and asymmetric configurations"
    - "feasibility of interference alignment for the mimo interference channel"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "cognitive wyner networks with clustered decoding"
    - "flexible backhaul design and degrees of freedom for linear interference networks"
    - "on the optimality of treating interference as noise"
    - "degrees of freedom of mimo cellular networks decomposition and linear beamforming design"
    - "degrees of freedom dof of locally connected interference channels with coordinated multi point comp transmission"

Connected through hyperedge HE-2 (induced by source paper "on the gaussian interference channel with half duplex causal cognition"):
  with members:
    - "on the capacity of the two user gaussian causal cognitive interference channel"
    - "on the capacity of interference channel with causal and noncausal generalized feedback at the cognitive transmitter"
    - "inner and outer bounds for the gaussian cognitive interference channel and new capacity results"
    - "wireless network information flow a deterministic approach"
    - "dependence balance based outer bounds for gaussian networks with cooperation and feedback"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "the two user gaussian interference channel a deterministic view"

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)