human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "extreme urllc vision challenges and key enablers"):
  Abstract: Notwithstanding the significant traction gained by ultra-reliable and low-latency communication (URLLC) in both academia and 3GPP standardization, fundamentals of URLLC remain elusive. Meanwhile, new immersive and high-stake control applications with much stricter reliability, latency and scalability requirements are posing unprecedented challenges in terms of system design and algorithmic solutions. This article aspires at providing a fresh and in-depth look into URLLC by first examining the limitations of 5G URLLC, and putting forward key research directions for the next generation of URLLC, coined eXtreme ultra-reliable and low-latency communication (xURLLC). xURLLC is underpinned by three core concepts: (1) it leverages recent advances in machine learning (ML) for faster and reliable data-driven predictions; (2) it fuses both radio frequency (RF) and non-RF modalities for modeling and combating rare events without sacrificing spectral efficiency; and (3) it underscores the much needed joint communication and control co-design, as opposed to the communication-centric 5G URLLC. The intent of this article is to spearhead beyond-5G/6G mission-critical applications by laying out a holistic vision of xURLLC, its research challenges and enabling technologies, while providing key insights grounded in selected use cases.

Includes members:
  Member "ultra reliable and low latency vehicular communication an active learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning assisted csi estimation for joint urllc and embb resource allocation")
  Member "massive autonomous uav path planning a neural network based mean field game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication efficient massive uav online path control federated learning meets mean field game theory")
  Member "one pixel image and rf signal based split learning for mmwave received power prediction":
    also connected through other hyperedges:
      (none)
  Member "time synchronization in 5g wireless edge requirements and solutions for critical mtc":
    also connected through other hyperedges:
      (none)
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "a statistical learning approach to ultra reliable low latency communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ultra reliable and low latency vehicular communication an active learning approach")

Question: Please tell me which class the source paper 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)