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 "sparse optimization for green edge ai inference"):
  Abstract: With the rapid upsurge of deep learning tasks at the network edge, effective edge artificial intelligence (AI) inference becomes critical to provide low-latency intelligent services for mobile users via leveraging the edge computing capability. In such scenarios, energy efficiency becomes a primary concern. In this paper, we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption, yielding a mixed combinatorial optimization problem. By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem. To solve this challenging problem, we propose a log-sum function based three-stage approach. By adopting the log-sum function to enhance the group sparsity, a proximal iteratively reweighted algorithm is developed. Furthermore, we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm. Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.

Includes members:
  Member "edge intelligence paving the last mile of artificial intelligence with edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning")
  Member "exploiting computation replication for mobile edge computing a fundamental computation communication tradeoff study":
    also connected through other hyperedges:
      (none)
  Member "edge ai on demand accelerating deep neural network inference via edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "mobile edge intelligence and computing for the internet of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data importance aware user scheduling for communication efficient edge machine learning")
  Member "energy efficient processing and robust wireless cooperative transmission for edge inference":
    also connected through other hyperedges:
      (none)
  Member "the roadmap to 6g ai empowered wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "data shuffling in wireless distributed computing via low rank optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the roadmap to 6g ai empowered wireless networks")

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)