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: "improvement of the han kobayashi rate region for general interference channel v2"
  Abstract: Allowing the input auxiliary random variables to be correlated and using the binning scheme, the Han-Kobayashi (HK) rate region for general interference channel is partially improved. The obtained partially new achievable rate region (i) is compared to the HK region and its simplified description, i.e., Chong-Motani-Garg (CMG) region, in a detailed and favorable manner, by considering different versions of the regions, and (ii) has an interesting and easy interpretation: as expected, any rate in our region has generally two additional terms in comparison with the HK region (one due to the input correlation and the other as a result of the binning scheme). Keywords. Interference channel, Input correlation, Binning scheme

Connected through hyperedge HE-1 (induced by source paper "independent signaling achieves the capacity region of the gaussian interference channel with common information to within one bit"):
  with members:
    - "the capacity region of the mimo interference channel and its reciprocity to within a constant gap"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"
    - "gaussian interference channel capacity to within one bit"
    - "interference channels with common information"

Connected through hyperedge HE-2 (induced by source paper "a new achievable rate region for the cognitive radio channel"):
  with members:
    - "state of the cognitive interference channel a new unified inner bound"
    - "on the capacity of interference channels with one cooperating transmitter"
    - "cognitive radio an information theoretic perspective"

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)