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: "a statistical learning approach to ultra reliable low latency communication"
  Abstract: Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to $10^{-9}$. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; (3) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.

Connected through hyperedge HE-1 (induced by source paper "extreme urllc vision challenges and key enablers"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "one pixel image and rf signal based split learning for mmwave received power prediction"
    - "time synchronization in 5g wireless edge requirements and solutions for critical mtc"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "wireless network intelligence at the edge"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"

Connected through hyperedge HE-2 (induced by source paper "ultra reliable and low latency vehicular communication an active learning approach"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "ultra reliable low latency vehicular networks taming the age of information tail"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "fine grained vs average reliability for v2v communications around intersections"
    - "gaussian processes for nonlinear signal processing an overview of recent advances"

Connected through hyperedge HE-3 (induced by source paper "enabling urllc for low cost iot devices via diversity combining schemes"):
  with members:
    - "joint power control and rate allocation enabling ultra reliability and energy efficiency in simo wireless networks"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "wide area wireless communication challenges for the internet of things"

Connected through hyperedge HE-4 (induced by source paper "ultra reliable and low latency communications using proactive multi cell association"):
  with members:
    - "wireless channel dynamics and robustness for ultra reliable low latency communications"
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "reliable transmission of short packets through queues and noisy channels under latency and peak age violation guarantees"
    - "energy latency tradeoff in ultra reliable low latency communication with retransmissions"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "heterogeneous networks with power domain noma coverage throughput and power allocation analysis"
    - "cross layer optimization for ultra reliable and low latency radio access networks"
    - "fundamentals of the downlink green coverage and energy efficiency in heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "a very brief introduction to machine learning with applications to communication systems"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "machine learning at the edge a data driven architecture with applications to 5g cellular networks"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"

Connected through hyperedge HE-6 (induced by source paper "wireless access in ultra reliable low latency communication urllc"):
  with members:
    - "how reliable and capable is multi connectivity"
    - "wireless channel dynamics and robustness for ultra reliable low latency communications"
    - "improving network availability of ultra reliable and low latency communications with multi connectivity"
    - "packet duplication in dual connectivity enabled 5g wireless networks overview and challenges"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "energy latency tradeoff in ultra reliable low latency communication with retransmissions"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"

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)