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: "ai assisted low information latency wireless networking"
  Abstract: The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and industrial Internet-of-things, many of which are categorized as ultra-Reliable Low-Latency Communications (uRLLC). In this article, an alternative view on uRLLC is presented, that information latency, which measures the distortion of information resulted from time lag of its acquisition process, is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for wireless neTworks (SMART) is presented to address the information latency optimization challenge. Case studies of typical applications in Autonomous Driving (AD) are demonstrated, i.e., dense platooning and intersection management, which show that SMART can effectively optimize information latency, and more importantly, information latency-optimized systems outperform conventional uRLLC-oriented systems significantly in terms of AD performance such as traffic efficiency, thus pointing out a new research and system design paradigm.

Connected through hyperedge HE-1 (induced by source paper "revealing much while saying less predictive wireless for status update"):
  with members:
    - "age of information vs value of information scheduling for cellular networked control systems"
    - "sampling for remote estimation through queues age of information and beyond"
    - "status from a random field how densely should one update"
    - "closed form whittle s index enabled random access for timely status update"
    - "minimizing the age of information in a csma environment"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "distributed scheduling algorithms for optimizing information freshness in wireless networks"

Connected through hyperedge HE-2 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing"):
  with members:
    - "cooperative service caching and workload scheduling in mobile edge computing"
    - "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"
    - "minimum age of information in the internet of things with non uniform status packet sizes"
    - "content popularity prediction towards location aware mobile edge caching"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "edge caching wireless networks performance analysis and optimization"
    - "edge caching in dense heterogeneous cellular networks with massive mimo aided self backhaul"

Connected through hyperedge HE-3 (induced by source paper "age of information optimized mac in v2x sidelink via piggyback based collaboration"):
  with members:
    - "analytical models of the performance of c v2x mode 4 vehicular communications"
    - "hierarchical soft slicing to meet multi dimensional qos demand in cache enabled vehicular networks"
    - "minimizing the age of information in a csma environment"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "study of the impact of phy and mac parameters in 3gpp c v2v mode 4"
    - "distributed scheduling algorithms for optimizing information freshness in wireless networks"
    - "age of information performance of multiaccess strategies with packet management"

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)