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: "a theory of network equivalence parts i and ii"
  Abstract: A family of equivalence tools for bounding network capacities is introduced. Part I treats networks of point-to-point channels. The main result is roughly as follows. Given a network of noisy, independent, memoryless point-to-point channels, a collection of communication demands can be met on the given network if and only if it can be met on another network where each noisy channel is replaced by a noiseless bit pipe with throughput equal to the noisy channel capacity. This result was known previously for the case of a single-source multicast demand. The result given here treats general demands -- including, for example, multiple unicast demands -- and applies even when the achievable rate region for the corresponding demands is unknown in the noiseless network. In part II, definitions of upper and lower bounding channel models for general channels are introduced. By these definitions, a collection of communication demands can be met on a network of independent channels if it can be met on a network where each channel is replaced by its lower bounding model and only if it can be met on a network where each channel is replaced by its upper bounding model. This work derives general conditions under which a network of noiseless bit pipes is an upper or lower bounding model for a multiterminal channel. Example upper and lower bounding models for broadcast, multiple access, and interference channels are given. It is then shown that bounding the difference between the upper and lower bounding models for a given channel yields bounds on the accuracy of network capacity bounds derived using those models. By bounding the capacity of a network of independent noisy channels by the network coding capacity of a network of noiseless bit pipes, this approach represents one step towards the goal of building computational tools for bounding network capacities.

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"
    - "capacity of multiple unicast in wireless networks a polymatroidal approach"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "soft decoding based strategies for relay and interference channels analysis and achievable rates using ldpc codes"):
  with members:
    - "on the performance of low density parity check codes for gaussian interference channels"
    - "mmse of bad codes"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "compute and forward harnessing interference through structured codes"
    - "joint physical layer coding and network coding for bi directional relaying"
    - "gaussian interference channel capacity to within one bit"
    - "network coding over a noisy relay a belief propagation approach"
    - "the generalized area theorem and some of its consequences"

Connected through hyperedge HE-3 (induced by source paper "on information flow and feedback in relay networks"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "improved capacity approximations for gaussian relay networks"
    - "efficient capacity computation and power optimization for relay networks"
    - "wireless network information flow a deterministic approach"
    - "wireless network simplification the gaussian n relay diamond network"
    - "approximately optimal wireless broadcasting"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "approximately achieving gaussian relay network capacity with lattice codes"

Connected through hyperedge HE-4 (induced by source paper "achieving the capacity of the n relay gaussian diamond network within log n bits"):
  with members:
    - "capacity approximations for gaussian relay networks"
    - "a unified relay framework with both d f and c f relay nodes"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "computation alignment capacity approximation without noise accumulation"
    - "wireless network simplification the gaussian n relay diamond network"
    - "on the capacity of the half duplex diamond channel"
    - "on the optimal compressions in the compress and forward relay schemes"

Connected through hyperedge HE-5 (induced by source paper "separation of source network coding and channel coding in wireline networks"):
  with members:
    - "on the separation of lossy source network coding and channel coding in wireline networks"
    - "optimality and approximate optimality of source channel separation in networks"

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)