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: "machine learning at the wireless edge distributed stochastic gradient descent over the air"
  Abstract: We study collaborative machine learning (ML) at the wireless edge, where power and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a remote parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and communicated to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless terminals, referred to as the workers, employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over the underlying wireless multiple access channel (MAC). We then introduce an analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation. In A-DSGD, the workers first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are transmitted directly over the MAC without employing any digital code. Numerical results show that A-DSGD converges much faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also observe that the performance of A-DSGD improves with the number of workers (keeping the total size of the dataset constant), while D-DSGD deteriorates, limiting the ability of the latter in harnessing the computation power of edge devices. The lack of quantization and channel encoding/decoding in A-DSGD further speeds up communication, making it very attractive for low-latency ML applications at the wireless network edge.

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"
    - "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 over wireless fading channels"
    - "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 "energy efficient federated edge learning with joint communication and computation design"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "energy efficient federated learning over wireless communication networks"
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "an overview of data importance aware radio resource management for edge machine learning"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "robust and communication efficient federated learning from non iid data"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-3 (induced by source paper "convergence time optimization for federated learning over wireless networks"):
  with members:
    - "energy efficient federated learning over wireless communication networks"
    - "accelerating dnn training in wireless federated edge learning system"
    - "towards federated learning at scale system design"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "federated neuromorphic learning of spiking neural networks for low power edge intelligence"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-4 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"):
  with members:
    - "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"
    - "broadband analog aggregation for low latency federated edge learning extended version"

Connected through hyperedge HE-5 (induced by source paper "federated over the air subspace learning from incomplete data"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "provable subspace tracking from missing data and matrix completion"
    - "federated learning over wireless fading channels"
    - "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-6 (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"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "accelerating dnn training in wireless federated edge learning system"
    - "federated learning over wireless fading channels"
    - "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-7 (induced by source paper "communication efficient federated learning over multiple access channels"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "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"
    - "federated learning over wireless fading channels"
    - "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-8 (induced by source paper "joint parameter and bandwidth allocation for improving the efficiency of partitioned edge learning"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "energy efficient radio resource allocation for federated edge learning"
    - "energy efficient federated learning over wireless communication networks"
    - "accelerating dnn training in wireless federated edge learning system"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "scheduling policies for federated learning in wireless networks"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "a joint learning and communications framework for federated learning over wireless networks"

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.DC(Distributed, Parallel, and Cluster Computing)