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: "fast algorithm for finding unicast capacity of linear deterministic wireless relay networks"
  Abstract: The deterministic channel model for wireless relay networks proposed by Avestimehr, Diggavi and Tse `07 has captured the broadcast and inference nature of wireless communications and has been widely used in approximating the capacity of wireless relay networks. The authors generalized the max-flow min-cut theorem to the linear deterministic wireless relay networks and characterized the unicast capacity of such deterministic network as the minimum rank of all the binary adjacency matrices describing source-destination cuts whose number grows exponentially with the size of the network. In this paper, we developed a fast algorithm for finding the unicast capacity of a linear deterministic wireless relay network by finding the maximum number of linearly independent paths using the idea of path augmentation. We developed a modified depth-first search algorithm tailored for the linear deterministic relay networks for finding linearly independent paths whose total number proved to equal the unicast capacity of the underlying network. The result of our algorithm suggests a capacity-achieving transmission strategy with one-bit length linear encoding at the relay nodes in the concerned linear deterministic wireless relay network. The correctness of our algorithm for universal cases is given by our proof in the paper. Moreover, our algorithm has a computational complexity bounded by $O(|{\cal{V}}_x|\cdot C^4+d\cdot |{\cal{V}}_x|\cdot C^3)$ which shows a significant improvement over the previous results for solving the same problem by Amaudruz and Fragouli (whose complexity is bounded by $O(M\cdot |{\cal{E}}|\cdot C^5)$ with $M\geq d$ and $|{\cal{E}}|\geq|{\cal{V}}_x|$) and by Yazdi and Savari (whose complexity is bounded by $O(L^8\cdot M^{12}\cdot h_0^3+L\cdot M^6\cdot C\cdot h_0^4)$ with $h_0\geq C$).

Connected through hyperedge HE-1 (induced by source paper "combinatiorial algorithms for wireless information flow"):
  with members:
    - "algebraic network coding approach to deterministic wireless relay networks"
    - "a combinatorial study of linear deterministic 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)