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: "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control"
  Abstract: As a revolution in networking, Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over tremendous IoT devices. This can enable a series of IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (functional) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analogwave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error. To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. Simulation demonstrates that controlling WPT provides additional design dimensions for substantially reducing the AirComp error.

Connected through hyperedge HE-1 (induced by source paper "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"):
  with members:
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "hybrid beamforming via the kronecker decomposition for the millimeter wave massive mimo systems"
    - "inference from randomized transmissions by many backscatter sensors"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "communication efficient learning of deep networks from decentralized data"
    - "over the air function computation in sensor networks"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "broadband analog aggregation for low latency federated edge learning extended version"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning via over the air computation"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "a survey on mobile edge computing the communication perspective"
    - "sparse communication for distributed gradient descent"
    - "revisiting distributed synchronous sgd"

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)