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: "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
  Abstract: One basic operation of Internet-of-Things (IoT) networks is aggregating distributed sensing-data over wireless-channels for function-computation, called wireless-data-aggregation (WDA). Targeting dense sensors, a recently developed technology called over-the-air computing (AirComp) can dramatically reduce the WDA latency by aggregating distributed data "over-the-air" using the waveform-superposition property of a multi-access channel. In this work, we design multiple-input-multiple-output (MIMO) AirComp for computing a vector-valued function in a clustered IoT network with multi-antenna sensors forming clusters and a multi-antenna access-point (AP) performing WDA. The resultant high-dimensional but low-rank MIMO channels makes it important to reduce channel or signal dimensionality in AirComp to avoid exposure to noise from channel null-spaces. Motivated by this, we develop a framework of reduced-dimension MIMO AirComp, featuring decomposed-aggregation-beamforming (DAB). Consider the case of separable channel-clusters with non-overlapping angle-of-arrival ranges. The optimal DAB has the structure where inner-components extract the dominant eigen-spaces of corresponding channel-clusters and outer-components jointly equalize the resultant low-dimensional channels. Consider the more complex case of inseparable clusters. We propose a suboptimal DAB design where the inner-component performs both dimension-reduction and joint-equalization over clustered-channel covariance matrices and the outer-component jointly equalizes the small-scale fading-channels. Furthermore, efficient algorithms for rank-optimization of individual DAB components and channel-feedback leveraging the AirComp principle are developed.

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 "simultaneous signal and interference alignment for two cell over the air computation"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "a survey on mobile edge computing the communication perspective"
    - "interference alignment for multicell multiuser mimo uplink channels"
    - "downlink interference alignment"

Connected through hyperedge HE-3 (induced by source paper "mobility assisted over the air computation for backscatter sensor networks"):
  with members:
    - "uav data collection over noma backscatter networks uav altitude and trajectory optimization"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "ambient backscatter communications a contemporary survey"

Connected through hyperedge HE-4 (induced by source paper "an introduction to communication efficient edge machine learning"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "data importance aware user scheduling for communication efficient edge machine learning"
    - "high dimensional stochastic gradient quantization for communication efficient edge learning"
    - "deep joint source channel coding for wireless image transmission"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "automatic recognition of space time constellations by learning on the grassmann manifold"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-5 (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"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "over the air function computation in sensor networks"
    - "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-6 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow"):
  with members:
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "over the air computation via intelligent reflecting surfaces"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "scheduling policies for federated learning in wireless networks"
    - "the roadmap to 6g ai empowered wireless networks"
    - "blind demixing for low latency communication"
    - "nonconvex demixing from bilinear measurements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"

Connected through hyperedge HE-7 (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"
    - "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"
    - "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)