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 "over the air computation systems optimization analysis and scaling laws"):
  Abstract: For future Internet of Things (IoT)-based Big Data applications (e.g., smart cities/transportation), wireless data collection from ubiquitous massive smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for edge fusion centers running computing tasks over large data sets with limited computation capacity. To tackle these challenges, by exploiting the superposition property of a multiple-access channel and the functional decomposition properties, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of $K$ sensors and one receiver (i.e., the fusion center). We consider an optimization problem to minimize the computation mean-squared error (MSE) of the $K$ sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of AirComp systems in terms of the average computation MSE and the average power consumption under Rayleigh fading channels with different Tx-Rx policies. For the computation-optimal policy, we prove that its average computation MSE has a decay rate of $O(1/\sqrt{K})$, and our numerical results illustrate that the policy also has a vanishing average power consumption with the increasing $K$, which jointly show the computation effectiveness and the energy efficiency of the policy with a large number of sensors.

Includes members:
  Member "machine learning at the wireless edge distributed stochastic gradient descent over the air":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "wireless networked control systems with coding free data transmission for industrial iot":
    also connected through other hyperedges:
      (none)
  Member "wireless federated distillation for distributed edge learning with heterogeneous data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "personalized federated learning for intelligent iot applications a cloud edge based framework")
  Member "optimal power control for over the air computation in fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "exploiting the superposition property of wireless communication for max consensus problems in multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a scalable max consensus protocol for noisy ultra dense networks")
  Member "towards an intelligent edge wireless communication meets machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "inference from randomized transmissions by many backscatter sensors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sum throughput maximization in multi tag backscattering to multiantenna reader")
  Member "over the air function computation in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis")

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)