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: "distributed federated learning for ultra reliable low latency vehicular communications"
  Abstract: In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide power consumption of vehicular users (VUEs) is minimized subject to high reliability in terms of probabilistic queuing delays. Using extreme value theory (EVT), a new reliability measure is defined to characterize extreme events pertaining to vehicles’ queue lengths exceeding a predefined threshold. To learn these extreme events, assuming they are independently and identically distributed over VUEs, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queue lengths. Considering the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed method to estimate the tail distribution of queues with an accuracy that is close to a centralized solution with up to 79% reductions in the amount of exchanged data. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline.

Connected through hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning"):
  with members:
    - "energy efficient federated learning over wireless communication networks"
    - "supervised learning under distributed features"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "revisiting distributed synchronous sgd"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "adding vs averaging in distributed primal dual optimization"
    - "fast distributed asynchronous sgd with variance reduction"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "how reliable and capable is multi connectivity"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "on device federated learning via blockchain and its latency analysis"
    - "mmwave beam prediction with situational awareness a machine learning approach"

Connected through hyperedge HE-3 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "one shot federated learning"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "robust and communication efficient collaborative learning"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"
    - "an exact quantized decentralized gradient descent algorithm"

Connected through hyperedge HE-4 (induced by source paper "eavesdrop the composition proportion of training labels in federated learning"):
  with members:
    - "a little is enough circumventing defenses for distributed learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "federated learning for emoji prediction in a mobile keyboard"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "multi party poisoning through generalized p tampering"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-5 (induced by source paper "a joint learning and communications framework for federated learning over wireless networks"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "fast uplink grant for noma a federated learning based approach"
    - "towards federated learning at scale system design"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "when machine learning meets big data a wireless communication perspective"
    - "multi hop federated private data augmentation with sample compression"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a crowdsourcing framework for on device federated learning"):
  with members:
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "online job scheduling in distributed machine learning clusters"
    - "federated optimization for heterogeneous networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "towards an intelligent edge wireless communication meets machine learning"

Connected through hyperedge HE-7 (induced by source paper "energy efficient federated learning over wireless communication networks"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "multi hop federated private data augmentation with sample compression"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "machine learning in the air"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing"):
  with members:
    - "a tutorial on computing t closeness"
    - "privacy preserving deep learning via weight transmission"
    - "a secure and robust scheme for sharing confidential information in iot systems"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "building scalable decentralized payment systems"
    - "chiron privacy preserving machine learning as a service"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"

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)