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: "underwater acoustic networks channel models and network coding based lower bound to transmission power for multicast"
  Abstract: The goal of this paper is two-fold. First, to establish a tractable model for the underwater acoustic channel useful for network optimization in terms of convexity. Second, to propose a network coding based lower bound for transmission power in underwater acoustic networks, and compare this bound to the performance of several network layer schemes. The underwater acoustic channel is characterized by a path loss that depends strongly on transmission distance and signal frequency. The exact relationship among power, transmission band, distance and capacity for the Gaussian noise scenario is a complicated one. We provide a closed-form approximate model for 1) transmission power and 2) optimal frequency band to use, as functions of distance and capacity. The model is obtained through numerical evaluation of analytical results that take into account physical models of acoustic propagation loss and ambient noise. Network coding is applied to determine a lower bound to transmission power for a multicast scenario, for a variety of multicast data rates and transmission distances of interest for practical systems, exploiting physical properties of the underwater acoustic channel. The results quantify the performance gap in transmission power between a variety of routing and network coding schemes and the network coding based lower bound. We illustrate results numerically for different network scenarios.

Connected through hyperedge HE-1 (induced by source paper "on the order optimality of large scale underwater networks"):
  with members:
    - "improved capacity scaling in wireless networks with infrastructure"
    - "linear capacity scaling in wireless networks beyond physical limits"
    - "capacity scaling laws for underwater networks"
    - "on capacity scaling in arbitrary wireless networks"
    - "information theoretic operating regimes of large wireless networks"
    - "on the relationship between transmission power and capacity of an underwater acoustic communication channel"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-2 (induced by source paper "on capacity scaling of underwater networks an information theoretic perspective"):
  with members:
    - "improved capacity scaling in wireless networks with infrastructure"
    - "capacity scaling laws for underwater networks"
    - "on capacity scaling in arbitrary wireless networks"
    - "information theoretic operating regimes of large wireless networks"
    - "on the relationship between transmission power and capacity of an underwater acoustic communication channel"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-3 (induced by source paper "capacity scaling laws for underwater networks"):
  with members:
    - "on the relationship between transmission power and capacity of an underwater acoustic communication channel"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc 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)