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: "algebraic network coding approach to deterministic wireless relay networks"
  Abstract: The deterministic wireless relay network model, introduced by Avestimehr et al., has been proposed for approximating Gaussian relay networks. This model, known as the ADT network model, takes into account the broadcast nature of wireless medium and interference. Avestimehr et al. showed that the Min-cut Max-flow theorem holds in the ADT network. #R##N#In this paper, we show that the ADT network model can be described within the algebraic network coding framework introduced by Koetter and Medard. We prove that the ADT network problem can be captured by a single matrix, called the "system matrix". We show that the min-cut of an ADT network is the rank of the system matrix; thus, eliminating the need to optimize over exponential number of cuts between two nodes to compute the min-cut of an ADT network. #R##N#We extend the capacity characterization for ADT networks to a more general set of connections. Our algebraic approach not only provides the Min-cut Max-flow theorem for a single unicast/multicast connection, but also extends to non-multicast connections such as multiple multicast, disjoint multicast, and two-level multicast. We also provide sufficiency conditions for achievability in ADT networks for any general connection set. In addition, we show that the random linear network coding, a randomized distributed algorithm for network code construction, achieves capacity for the connections listed above. #R##N#Finally, we extend the ADT networks to those with random erasures and cycles (thus, allowing bi-directional links). Note that ADT network was proposed for approximating the wireless networks; however, ADT network is acyclic. Furthermore, ADT network does not model the stochastic nature of the wireless links. With our algebraic framework, we incorporate both cycles as well as random failures into ADT network model.

Connected through hyperedge HE-1 (induced by source paper "bounded contention coding for wireless networks in the high snr regime"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "deterministic network model revisited an algebraic network coding approach"
    - "faster information dissemination in dynamic networks via network coding"
    - "improved combinatorial algorithms for wireless information flow"
    - "collision helps algebraic collision recovery for wireless erasure networks"
    - "analyzing network coding gossip made easy"
    - "dynamic sharing of a multiple access channel"

Connected through hyperedge HE-2 (induced by source paper "two unicast information flows over linear deterministic networks"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "interference mitigation through limited transmitter cooperation"
    - "interference mitigation through limited receiver cooperation"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-3 (induced by source paper "deterministic network model revisited an algebraic network coding approach"):
  with members:
    - "a deterministic approach to wireless relay networks"
    - "gaussian interference channel capacity to within one bit"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-4 (induced by source paper "a deterministic polynomial time algorithm for constructing a multicast coding scheme for linear deterministic relay networks"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "a combinatorial study of linear deterministic relay networks"
    - "approximate capacity of gaussian relay networks"

Connected through hyperedge HE-5 (induced by source paper "approximately optimal wireless broadcasting"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "a digital interface for gaussian relay and interference networks lifting codes from the discrete superposition model"
    - "noisy network coding"
    - "linear finite field deterministic networks with many sources and one destination"
    - "reciprocity in linear deterministic networks under linear coding"
    - "on approximating gaussian relay networks by deterministic networks"
    - "a combinatorial study of linear deterministic relay networks"
    - "cooperative relay broadcast channels"

Connected through hyperedge HE-6 (induced by source paper "combinatiorial algorithms for wireless information flow"):
  with members:
    - "a combinatorial study of linear deterministic relay networks"
    - "fast algorithm for finding unicast capacity of linear deterministic wireless relay networks"
    - "approximate capacity of gaussian relay networks"
    - "a deterministic approach to wireless relay networks"
    - "wireless network information flow"

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)