human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "energy efficient resource allocation for hybrid services with future channel gains"):
  Abstract: In this paper, we propose a framework to maximize energy efficiency (EE) of a system supporting real-time (RT) and non-real-time services by exploiting future average channel gains of mobile users, which change in the timescale of seconds and are reported predictable within a minute-long time window. To demonstrate the potential of improving EE by jointly optimizing resource allocation for both services by harnessing both future average channel gains and current instantaneous channel gains, we optimize a two-timescale policy with perfect prediction, by taking orthogonal frequency division multiple access system serving RT and video-on-demand (VoD) users as an example. Considering that fine-grained prediction for every user is with high cost, we propose a heuristic policy that only needs to predict the median of average channel gains of VoD users. Simulation results show that the optimal policy outperforms relevant counterparts, indicating the necessity of the joint optimization for both services and for two timescales. Besides, the heuristic policy performs closely to the optimal policy with perfect prediction while becomes superior with large prediction errors. This suggests that the EE gain over non-predictive policies can be captured with coarse-grained prediction.

Includes members:
  Member "an lstm network for highway trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "energy efficient resource allocation in machine to machine communications with multiple access and energy harvesting for iot":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interplay between noma and other emerging technologies a survey")
  Member "fundamental green tradeoffs progresses challenges and impacts on 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beamforming optimization for intelligent reflecting surface assisted mimo a sum path gain maximization approach")
  Member "anticipatory radio resource management for mobile video streaming with linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks")
  Member "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "adaptive video streaming for wireless networks with multiple users and helpers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wireless caching helper system with heterogeneous traffic and random availability")
  Member "energy efficient adaptive video transmission exploiting rate predictions in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "power control for wireless vbr video streaming from optimization to reinforcement learning")

Question: Please tell me which class the source paper 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)