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: "over the air function computation in sensor networks"
  Abstract: Many sensor applications are interested in computing a function over measurements (e.g., sum, average, max) as opposed to collecting all sensor data. Today, such data aggregation is done in a cluster-head. Sensor nodes transmit their values sequentially to a cluster-head node, which calculates the aggregation function and forwards it to the base station. In contrast, this paper explores the possibility of computing a desired function over the air. We devise a solution that enables sensors to transmit coherently over the wireless medium so that the cluster-head directly receives the value of the desired function. We present analysis and preliminary results that demonstrate that such a design yield a large improvement in network throughput.

Connected through hyperedge HE-1 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-3 (induced by source paper "over the air computation systems optimization analysis and scaling laws"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "wireless networked control systems with coding free data transmission for industrial iot"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "optimal power control for over the air computation in fading channels"
    - "exploiting the superposition property of wireless communication for max consensus problems in multi agent systems"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "inference from randomized transmissions by many backscatter sensors"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"

Connected through hyperedge HE-4 (induced by source paper "optimal power control for over the air computation in fading channels"):
  with members:
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "on ergodic sum capacity of fading cognitive multiple access and broadcast channels"
    - "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity"
    - "compute and forward harnessing interference through structured codes"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-5 (induced by source paper "computing and communicating functions in disorganized wireless networks"):
  with members:
    - "upper bound on function computation in directed acyclic networks"
    - "nomographic functions efficient computation in clustered gaussian sensor networks"
    - "computation over gaussian networks with orthogonal components"
    - "computing linear functions by linear coding over networks"
    - "duty cycle aware minimum energy multicasting in wireless sensor networks"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-6 (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"
    - "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control"
    - "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"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-7 (induced by source paper "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control"):
  with members:
    - "inference from randomized transmissions by many backscatter sensors"
    - "communications and signals design for wireless power transmission"
    - "resource allocation in wireless powered relay networks a bargaining game approach"
    - "wireless powered communication opportunities and challenges"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "energy harvesting for physical layer security in ofdma networks"
    - "simultaneous wireless information and power transfer under different csi acquisition schemes"
    - "cutting last wires for mobile communication by microwave power transfer"

Connected through hyperedge HE-8 (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"
    - "wirelessly powered data aggregation for iot via over the air functional computation beamforming and power control"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "a survey on mobile edge computing the communication perspective"
    - "sparse communication for distributed gradient descent"

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.NI(Networking and Internet Architecture)