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: "federated learning over wireless fading channels"
  Abstract: We study federated machine learning at the wireless network edge, where limited power wireless devices, each with its own dataset, build a joint model with the help of a remote parameter server (PS). We consider a bandwidth-limited fading multiple access channel (MAC) from the wireless devices to the PS, and implement distributed stochastic gradient descent (DSGD) over-the-air. We first propose a digital DSGD (D-DSGD) scheme, in which one device is selected opportunistically for transmission at each iteration based on the channel conditions; the scheduled device quantizes its gradient estimate to a finite number of bits imposed by the channel condition, and transmits these bits to the PS in a reliable manner. Next, motivated by the additive nature of the wireless MAC, we propose a novel analog communication scheme, referred to as the compressed analog DSGD (CA-DSGD), where the devices first sparsify their gradient estimates while accumulating error from previous iterations, and project the resultant sparse vector into a low-dimensional vector. We also design a power allocation scheme to align the received gradient vectors at the PS in an efficient manner. Numerical results show that the proposed CA-DSGD algorithm converges much faster than the D-DSGD scheme and other schemes in the literature, while providing a significantly higher accuracy.

Connected through hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "differential privacy enabled federated learning for sensitive health data"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning with bayesian differential privacy"
    - "on analog gradient descent learning over multiple access fading channels"

Connected through hyperedge HE-2 (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"
    - "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"
    - "broadband analog aggregation for low latency federated edge learning extended version"

Connected through hyperedge HE-3 (induced by source paper "federated over the air subspace learning from incomplete data"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "provable subspace tracking from missing data and matrix completion"
    - "federated pca with adaptive rank estimation"
    - "federated machine learning concept and applications"
    - "machine learning in the air"
    - "byzantine stochastic gradient descent"
    - "federated learning via over the air computation"

Connected through hyperedge HE-4 (induced by source paper "update aware device scheduling for federated learning at the wireless edge"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "accelerating dnn training in wireless federated edge learning system"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "age based scheduling policy for federated learning in mobile edge networks"
    - "scheduling policies for federated learning in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"

Connected through hyperedge HE-5 (induced by source paper "communication efficient federated learning over multiple access channels"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "on analog gradient descent learning over multiple access fading channels"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "federated learning via over the air computation"

Connected through hyperedge HE-6 (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"
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "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"
    - "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-7 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"

Connected through hyperedge HE-8 (induced by source paper "computation scheduling for distributed machine learning with straggling workers"):
  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 coded computation"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "polynomially coded regression optimal straggler mitigation via data encoding"

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)