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: "mixed noisy network coding and cooperative unicasting in wireless networks"
  Abstract: The problem of communicating a single message to a destination in presence of multiple relay nodes, referred to as cooperative unicast network, is considered. First, we introduce "Mixed Noisy Network Coding" (MNNC) scheme which generalizes "Noisy Network Coding" (NNC) where relays are allowed to decode-and-forward (DF) messages while all of them (without exception) transmit noisy descriptions of their observations. These descriptions are exploited at the destination and the DF relays aim to decode the transmitted messages while creating full cooperation among the nodes. Moreover, the destination and the DF relays can independently select the set of descriptions to be decoded or treated as interference. This concept is further extended to multi-hopping scenarios, referred to as "Layered MNNC" (LMNNC), where DF relays are organized into disjoint groups representing one hop in the network. For cooperative unicast additive white Gaussian noise (AWGN) networks we show that -provided DF relays are properly chosen- MNNC improves over all previously established constant gaps to the cut-set bound. Secondly, we consider the composite cooperative unicast network where the channel parameters are randomly drawn before communication starts and remain fixed during the transmission. Each draw is assumed to be unknown at the source and fully known at the destination but only partly known at the relays. We introduce through MNNC scheme the concept of "Selective Coding Strategy" (SCS) that enables relays to decide dynamically whether, in addition to communicate noisy descriptions, is possible to decode and forward messages. It is demonstrated through slow-fading AWGN relay networks that SCS clearly outperforms conventional coding schemes.

Connected through hyperedge HE-1 (induced by source paper "capacity bounds for a class of interference relay channels"):
  with members:
    - "new outer bounds for the interference channel with unilateral source cooperation"
    - "a new achievable scheme for interference relay channels"
    - "two birds and one stone gaussian interference channel with a shared out of band relay of limited rate"
    - "wireless network information flow a deterministic approach"
    - "incremental relaying for the gaussian interference channel with a degraded broadcasting relay"
    - "interference channels with source cooperation"
    - "the gaussian interference relay channel improved achievable rates and sum rate upperbounds using a potent relay"
    - "interference channels with destination cooperation"

Connected through hyperedge HE-2 (induced by source paper "short message noisy network coding with a decode forward option"):
  with members:
    - "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows"
    - "a unified relay framework with both d f and c f relay nodes"
    - "wireless network information flow a deterministic approach"
    - "on compress forward without wyner ziv binning for relay networks"
    - "short message quantize forward network coding"
    - "slepian wolf coding over cooperative relay networks"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "noisy network coding"

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)