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: "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
  Abstract: Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromising their privacy. In the FEEL framework, edge devices periodically transmit high-dimensional stochastic gradients to the edge server, where these gradients are aggregated and used to update a global model. When the edge devices share the same communication medium, the multiple access channel from the devices to the edge server induces a communication bottleneck. To overcome this bottleneck, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients (or local models) via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation (OBDA). The new scheme features one-bit gradient quantization followed by digital modulation at the edge devices and a majority-voting based decoding at the edge server. We develop a comprehensive analysis framework for quantifying the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, we show that all the negative effects vanish as the number of participating devices grows, but at a different rate for each type of channel hostility.

Connected through hyperedge HE-1 (induced by source paper "decentralized federated learning via sgd over wireless d2d networks"):
  with members:
    - "distributed learning in the non convex world from batch to streaming data and beyond"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "advances and open problems in federated learning"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "an introduction to decentralized stochastic optimization with gradient tracking"
    - "federated learning over wireless fading channels"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-2 (induced by source paper "accelerating dnn training in wireless federated edge learning system"):
  with members:
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "on device federated learning via blockchain and its latency analysis"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "sparse binary compression towards distributed deep learning with minimal communication"

Connected through hyperedge HE-3 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow"):
  with members:
    - "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"
    - "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"

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)