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: "energy efficient scheduling for loss tolerant iot applications with uninformed transmitter"
  Abstract: In this work we investigate energy efficient packet scheduling problem for the loss tolerant applications. We consider slow fading channel for a point to point connection with no channel state information at the transmitter side (CSIT). In the absence of CSIT, the slow fading channel has an outage probability associated with every transmit power. As a function of data loss tolerance parameters and peak power constraints, we formulate an optimization problem to minimize the average transmit energy for the user equipment (UE). The optimization problem is not convex and we use stochastic optimization technique to solve the problem. The numerical results quantify the effect of different system parameters on average transmit power and show significant power savings for the loss tolerant applications.

Connected through hyperedge HE-1 (induced by source paper "optimal and low complexity dynamic spectrum access for rf powered ambient backscatter system with online reinforcement learning"):
  with members:
    - "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter"
    - "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
    - "inference from randomized transmissions by many backscatter sensors"
    - "ambient backscatter communications a contemporary survey"
    - "mimo wiretap channel under receiver side power constraints with applications to wireless power transfer and cognitive radio"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"
    - "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis"
    - "opportunistic wireless energy harvesting in cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "on optimizing power allocation for reliable communication over fading channels with uninformed transmitter"):
  with members:
    - "energy efficient packet scheduling with finite blocklength codes convexity analysis and efficient algorithms"
    - "energy and bursty packet loss tradeoff over fading channels a system level model"
    - "downlink transmission of short packets framing and control information revisited"
    - "asymptotically optimal policies for hard deadline scheduling over fading channels"
    - "energy efficient scheduling of delay constrained traffic over fading channels"

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)