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: "information theory vs queueing theory for resource allocation in multiple access channels"
  Abstract: We consider the problem of rate allocation in a fading Gaussian multiple-access channel with fixed transmission powers. The goal is to maximize a general concave utility function of the expected achieved rates of the users. There are different approaches to this problem in the literature. From an information theoretic point of view, rates are allocated only by using the channel state information. The queueing theory approach utilizes the global queue-length information for rate allocation to guarantee throughput optimality as well as maximizing a utility function of the rates. In this work, we make a connection between these two approaches by showing that the information theoretic capacity region of a multiple-access channel and its stability region are equivalent. Moreover, our numerical results show that a simple greedy policy which does not use the queue-length information can outperform queue-length based policies in terms of convergence rate and fairness.

Connected through hyperedge HE-1 (induced by source paper "sensing decision based cooperative relaying schemes with multi access transmission stability region and average delay characterization"):
  with members:
    - "cooperative access in cognitive radio networks stable throughput and delay tradeoffs"
    - "stable throughput in a cognitive wireless network"

Connected through hyperedge HE-2 (induced by source paper "information capacity of an energy harvesting sensor node"):
  with members:
    - "energy management policies for energy neutral source channel coding"
    - "awgn channel under time varying amplitude constraints with causal information at the transmitter"
    - "towards a communication theoretic understanding of system level power consumption"
    - "information capacity of energy harvesting sensor nodes"
    - "basic performance limits and tradeoffs in energy harvesting sensor nodes with finite data and energy storage"
    - "information theoretic analysis of an energy harvesting communication system"
    - "efficient energy management policies for networks with energy harvesting sensor nodes"
    - "the price of certainty waterslide curves and the gap to capacity"

Connected through hyperedge HE-3 (induced by source paper "distributed stochastic power control in ad hoc networks a nonconvex optimization case"):
  with members:
    - "mapel achieving global optimality for a non convex wireless power control problem"

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)