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: "semantic effectiveness filtering and control for post 5g wireless connectivity"
  Abstract: The traditional role of a communication engineer is to address the technical problem of transporting bits reliably over a noisy channel. With the emergence of 5G, and the availability of a variety of competing and coexisting wireless systems, wireless connectivity is becoming a commodity. This article argues that communication engineers in the post-5G era should extend the scope of their activity in terms of design objectives and constraints beyond connectivity to encompass the semantics of the transferred bits within the given applications and use cases. To provide a platform for semantic-aware connectivity solutions, this paper introduces the concept of a semantic-effectiveness (SE) plane as a core part of future communication architectures. The SE plane augments the protocol stack by providing standardized interfaces that enable information filtering and direct control of functionalities at all layers of the protocol stack. The advantages of the SE plane are described in the perspective of recent developments in 5G, and illustrated through a number of example applications. The introduction of a SE plane may help replacing the current "next-G paradigm" in wireless evolution with a framework based on continuous improvements and extensions of the systems and standards.

Connected through hyperedge HE-1 (induced by source paper "data importance aware user scheduling for communication efficient edge machine learning"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "optimizing pipelined computation and communication for latency constrained edge learning"
    - "accelerating dnn training in wireless federated edge learning system"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "towards federated learning at scale system design"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "active learning solution on distributed edge computing"
    - "label less learning for traffic control in an edge network"

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.NI(Networking and Internet Architecture)