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: "wireless network coding via modified 802 11 mac phy design and implementation on sdr"
  Abstract: Network coding (NC), in principle, is a Layer-3 innovation that improves network throughput in wired networks for multicast/broadcast scenarios. Due to the fundamental differences between wired and wireless networks, extending NC to wireless networks generates several new and significant practical challenges. Two-way information exchange (both symmetric and asymmetric) between a pair of 802.11 sources/sinks using an intermediate relay node is a canonical scenario for evaluating the effectiveness of Wireless Network Coding (WNC) in a practical setting. Our primary objective in this work is to suggest pragmatic and novel modifications at the MAC and PHY layers of the 802.11 protocol stack on a Software Radio (SORA) platform to support WNC and obtain achievable throughput estimates via lab-scale experiments. Our results show that network coding (at the MAC or PHY layer) increases system throughput-typically by 20-30%.

Connected through hyperedge HE-1 (induced by source paper "implementation of uplink network coded modulation for two hop networks"):
  with members:
    - "complex linear physical layer network coding"
    - "asynchronous convolutional coded physical layer network coding"
    - "network coded multiple access"
    - "asynchronous physical layer network coding"
    - "implementation of physical layer network coding"
    - "physical layer network coding"

Connected through hyperedge HE-2 (induced by source paper "simulation study on collaborative content distribution in delay tolerant vehicular networks"):
  with members:
    - "collaborative downloading in vanet using network coding"

Connected through hyperedge HE-3 (induced by source paper "optimal rate diverse wireless network coding"):
  with members:
    - "lattice codes achieve the capacity of common message gaussian broadcast channels with coded side information"
    - "lattice index coding"
    - "on index coding in noisy broadcast channels with receiver message side information"
    - "compute and forward harnessing interference through structured codes"
    - "broadcast capacity region of two phase bidirectional relaying"
    - "low density lattice codes"

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)