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: "fast uplink grant for machine type communications challenges and opportunities"
  Abstract: The notion of a fast uplink grant is emerging as a promising solution for enabling massive machine type communications (MTCs) in the Internet of Things over cellular networks. By using the fast uplink grant, machine type devices (MTD) will no longer require random access (RA) channels to send scheduling requests. Instead, uplink resources can be actively allocated to MTDs by the base station. In this paper, the challenges and opportunities for adopting the fast uplink grant to support MTCs are investigated. First, the fundamentals of fast uplink grant and its advantages over conventional access schemes: i) fully scheduled with RA process and ii) uncoordinated access, are presented. Then, the key challenges that include the prediction of set of MTDs with data to transmit, as well as the optimal scheduling of MTDs, are exposed. To overcome these challenges, a two-stage approach that includes traffic prediction and optimized scheduling is proposed. For this approach, various solutions for source traffic prediction for periodic MTD traffic are reviewed and novel methods for event-driven traffic prediction are proposed. For optimal allocation of uplink grants, new solutions based on advanced machine learning methods are presented. By using the proposed solutions, the fast uplink grant has the potential to enable cellular networks to support massive MTCs and effectively reduce the signaling overhead and overcome the delay and congestion challenges of conventional RA schemes.

Connected through hyperedge HE-1 (induced by source paper "massive access in beyond 5g iot networks with noma np hardness competitiveness and learning"):
  with members:
    - "energy efficient joint user rb association and power allocation for uplink hybrid noma oma"
    - "delay constrained input queued switch"
    - "dynamic user clustering and power allocation for uplink and downlink non orthogonal multiple access noma systems"
    - "probabilistic rateless multiple access for machine to machine communication"

Connected through hyperedge HE-2 (induced by source paper "contextual bandit learning for machine type communications in the null space of multi antenna systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"
    - "a directed information learning framework for event driven m2m traffic prediction"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "random access schemes in wireless systems with correlated user activity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "distributed learning for low latency machine type communication in a massive internet of things"

Connected through hyperedge HE-3 (induced by source paper "six key enablers for machine type communication in 6g"):
  with members:
    - "6g the next frontier"
    - "massive machine type communication mmtc access with integrated authentication"
    - "energy harvesting wireless communications a review of recent advances"
    - "coded random access applying codes on graphs to design random access protocols"

Connected through hyperedge HE-4 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  with members:
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "random access schemes in wireless systems with correlated user activity"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "grant free massive noma outage probability and throughput"

Connected through hyperedge HE-5 (induced by source paper "deep learning for signal authentication and security in massive internet of things systems"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "colonel blotto game for secure state estimation in interdependent critical infrastructure"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-6 (induced by source paper "a directed information learning framework for event driven m2m traffic prediction"):
  with members:
    - "random access schemes in wireless systems with correlated user activity"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "universal estimation of directed information"

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)