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: "towards an intelligent edge wireless communication meets machine learning"
  Abstract: The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing (MEC) platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified.

Connected through hyperedge HE-1 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach"):
  with members:
    - "model pruning enables efficient federated learning on edge devices"
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "advances and open problems in federated learning"
    - "introduction to online convex optimization"
    - "federated learning challenges methods and future directions"
    - "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"

Connected through hyperedge HE-2 (induced by source paper "convergence time optimization for federated learning over wireless networks"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "secel privacy preserving verifiable and fault tolerant edge learning for autonomous vehicles"):
  with members:
    - "advances and open problems in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "how to backdoor federated learning"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"

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"
    - "broadband analog aggregation for low latency federated edge learning extended version"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (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"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "adaptive federated learning in resource constrained edge computing systems"

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)