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: "capacity of multiple unicast in wireless networks a polymatroidal approach"
  Abstract: A classical result in undirected wireline networks is the near optimality of routing (flow) for multiple-unicast traffic (multiple sources communicating independent messages to multiple destinations): the min cut upper bound is within a logarithmic factor of the number of sources of the max flow. In this paper we "extend" the wireline result to the wireless context. #R##N#Our main result is the approximate optimality of a simple layering principle: {\em local physical-layer schemes combined with global routing}. We use the {\em reciprocity} of the wireless channel critically in this result. Our formal result is in the context of channel models for which "good" local schemes, that achieve the cut-set bound, exist (such as Gaussian MAC and broadcast channels, broadcast erasure networks, fast fading Gaussian networks). #R##N#Layered architectures, common in the engineering-design of wireless networks, can have near-optimal performance if the {\em locality} over which physical-layer schemes should operate is carefully designed. Feedback is shown to play a critical role in enabling the separation between the physical and the network layers. The key technical idea is the modeling of a wireless network by an undirected "polymatroidal" network, for which we establish a max-flow min-cut approximation theorem.

Connected through hyperedge HE-1 (induced by source paper "scalable capacity bounding models for wireless networks"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "a generalized typicality for abstract alphabets"
    - "distributed channel synthesis"
    - "wireless network information flow a deterministic approach"
    - "separation of source network coding and channel coding in wireline networks"
    - "path gain algebraic formulation for the scalar linear network coding problem"
    - "optimality and approximate optimality of source channel separation in networks"
    - "a theory of network equivalence parts i and ii"

Connected through hyperedge HE-2 (induced by source paper "near optimal quantization and linear network coding for relay networks"):
  with members:
    - "an algebraic approach to physical layer network coding"
    - "expansion coding achieving the capacity of an aen channel"
    - "multicommodity flows and cuts in polymatroidal networks"
    - "wireless network information flow a deterministic approach"
    - "low complexity near optimal codes for gaussian relay networks"
    - "binary error correcting network codes"
    - "a digital interface for gaussian relay and interference networks lifting codes from the discrete superposition model"
    - "approximately achieving gaussian relay network capacity with lattice codes"

Connected through hyperedge HE-3 (induced by source paper "multicommodity flows and cuts in polymatroidal networks"):
  with members:
    - "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows"
    - "wireless network information flow a deterministic approach"
    - "approximation algorithms for submodular multiway partition"
    - "submodular cost allocation problem and applications"
    - "submodular functions extensions distributions and algorithms a survey"

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)