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: "broadband analog aggregation for low latency federated edge learning extended version"
  Abstract: The popularity of mobile devices results in the availability of enormous data and computational resources at the network edge. To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low latency multi-access scheme for edge learning. We consider a popular framework, federated edge learning (FEEL), where edge-server and on-device learning are synchronized to train a model without violating user-data privacy. It is proposed that model updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the superposition property of a multi-access channel. Thereby, "interference" is harnessed to provide fast implementation of the model aggregation. This results in dramatical latency reduction compared with the traditional orthogonal access (i.e., OFDMA). In this work, the performance of FEEL is characterized targeting a single-cell random network. First, due to power alignment between devices as required for aggregation, a fundamental tradeoff is shown to exist between the update-reliability and the expected update-truncation ratio. This motivates the design of an opportunistic scheduling scheme for FEEL that selects devices within a distance threshold. This scheme is shown using real datasets to yield satisfactory learning performance in the presence of high mobility. Second, both the multi-access latency of the proposed analog aggregation and the OFDMA scheme are analyzed. Their ratio, which quantifies the latency reduction of the former, is proved to scale almost linearly with device population.

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 over wireless fading channels"
    - "federated learning with bayesian differential privacy"

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"
    - "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-3 (induced by source paper "learning from peers at the wireless edge"):
  with members:
    - "decentralized federated learning a segmented gossip approach"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "a federated learning approach for mobile packet classification"
    - "peer to peer federated learning on graphs"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "agnostic federated learning"
    - "scheduling policies for federated learning in wireless networks"
    - "wireless network intelligence at the edge"

Connected through hyperedge HE-4 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-5 (induced by source paper "machine learning at the wireless edge distributed stochastic gradient descent over the air"):
  with members:
    - "computation scheduling for distributed machine learning with straggling workers"
    - "hierarchical coded computation"
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "federated learning via over the air computation"
    - "sparse binary compression towards distributed deep learning with minimal communication"

Connected through hyperedge HE-6 (induced by source paper "accelerating dnn training in wireless federated edge learning system"):
  with members:
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "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"
    - "sparse binary compression towards distributed deep learning with minimal communication"

Connected through hyperedge HE-7 (induced by source paper "scheduling policies for federated learning in wireless networks"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning via over the air computation"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "towards an intelligent edge wireless communication meets machine learning"

Connected through hyperedge HE-8 (induced by source paper "federated learning over wireless fading channels"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "federated learning via over the air computation"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "variance based gradient compression for efficient distributed deep learning"
    - "don t use large mini batches use local sgd"
    - "signsgd compressed optimisation for non convex problems"

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)