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: "correlation aware resource allocation in multi cell networks"
  Abstract: We propose a cross-layer strategy for resource allocation between spatially correlated sources in the uplink of multi-cell FDMA networks. Our objective is to find the optimum power and channel to sources, in order to minimize the maximum distortion achieved by any source in the network. Given that the network is multi-cell, the inter-cell interference must also be taken into consideration. This resource allocation problem is NP-hard and the optimal solution can only be found by exhaustive search over the entire solution space, which is not computationally feasible. We propose a three step method to be performed separately by the scheduler in each cell, which finds cross-layer resource allocation in simple steps. The three- step algorithm separates the problem into inter-cell resource management, grouping of sources for joint decoding, and intra- cell channel assignment. For each of the steps we propose allocation methods that satisfy different design constraints. In the simulations we compare methods for each step of the algorithm. We also demonstrate the overall gain of using correlation-aware resource allocation for a typical multi-cell network of Gaussian sources. We show that, while using correlation in compression and joint decoding can achieve 25% loss in distortion over independent decoding, this loss can be increased to 37% when correlation is also utilized in resource allocation method. This significant distortion loss motivates further work in correlation-aware resource allocation. Overall, we find that our method achieves a 60% decrease in 5 percentile distortion compared to independent methods.

Connected through hyperedge HE-1 (induced by source paper "correlation aware packet scheduling in multi camera networks"):
  with members:
    - "interactive multiview video system with non complex navigation at the decoder"

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)