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: "on device federated learning via blockchain and its latency analysis"
  Abstract: In this letter, we propose a block-chained federated learning (BlockFL) architecture, where mobile devices' local learning model updates are exchanged and verified by leveraging blockchain. This enables on-device machine learning without any central coordination, even when each device lacks its own training data samples. We investigate the end-to-end learning completion latency of BlockFL, thereby yielding the optimal block generation rate as well as important insights in terms of network scalability and robustness.

Connected through hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "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"
    - "mmwave beam prediction with situational awareness a machine learning approach"

Connected through hyperedge HE-2 (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"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "a crowdsourcing framework for on device federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "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-6 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "securing distributed machine learning in high dimensions"
    - "entity resolution and federated learning get a federated resolution"
    - "how to backdoor federated learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "federated meta learning for recommendation"
    - "federated learning with non iid data"

Connected through hyperedge HE-7 (induced by source paper "baffle blockchain based aggregator free federated learning"):
  with members:
    - "decentralized and collaborative ai on blockchain"
    - "towards federated learning at scale system design"
    - "peer to peer federated learning on graphs"
    - "federated learning strategies for improving communication efficiency"
    - "how to backdoor federated learning"
    - "blockchained on device federated learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "research challenges for enterprise cloud computing"

Connected through hyperedge HE-8 (induced by source paper "galaxy learning a position paper"):
  with members:
    - "federated machine learning concept and applications"
    - "federated learning strategies for improving communication efficiency"
    - "blockchained on device federated learning"
    - "communication efficient learning of deep networks from decentralized data"

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)