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: "bounds on the network coding capacity for wireless random networks"
  Abstract: Recently, it has been shown that the max flow capacity can be achieved in a multicast network using network coding. In this paper, we propose and analyze a more realistic model for wireless random networks. We prove that the capacity of network coding for this model is concentrated around the expected value of its minimum cut. Furthermore, we establish upper and lower bounds for wireless nodes using Chernoff bound. Our experiments show that our theoretical predictions are well matched by simulation results.

Connected through hyperedge HE-1 (induced by source paper "on the delay advantage of coding in packet erasure networks"):
  with members:
    - "coding delay analysis of chunked codes over line networks"
    - "how fast can dense codes achieve the min cut capacity of line networks"
    - "on the delay of network coding over line networks"
    - "wireless erasure networks with feedback"
    - "network coding capacity of random wireless networks under a sinr model"
    - "coding schemes for line networks"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-2 (induced by source paper "on the delay of network coding over line networks"):
  with members:
    - "network coding capacity of random wireless networks under a sinr model"
    - "coding schemes for line networks"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-3 (induced by source paper "network coding based protection strategies against a single link failure in optical networks"):
  with members:
    - "network protection codes against link failures using network coding"
    - "random linear network coding a free cipher"
    - "network coding capacity of random wireless networks under a signal to interference and noise model"

Connected through hyperedge HE-4 (induced by source paper "network protection codes providing self healing in autonomic networks using network coding"):
  with members:
    - "protection schemes for two link failures in optical networks"
    - "network coding based protection strategies against a single link failure in optical networks"
    - "network protection codes against link failures using network coding"

Connected through hyperedge HE-5 (induced by source paper "on the capacity improvement of multicast traffic with network coding"):
  with members:
    - "network coding capacity of random wireless networks under a signal to interference and noise model"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-6 (induced by source paper "near optimal broadcast with network coding in large sensor networks"):
  with members:
    - "minimum cost multicast over coded packet networks"
    - "further results on coding for reliable communication over packet networks"
    - "on coding for reliable communication over packet 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)