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: "deep learning framework for wireless systems applications to optical wireless communications"
  Abstract: Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.

Connected through hyperedge HE-1 (induced by source paper "scheduling policies for federated learning in wireless networks"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-2 (induced by source paper "robust federated learning with noisy communication"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "scheduling policies for federated learning in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "deep learning for distributed optimization applications to wireless resource management"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "machine learning in the air"
    - "learning to optimize training deep neural networks for interference management"
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "optnet differentiable optimization as a layer in neural networks"
    - "the expressive power of neural networks a view from the width"
    - "proactive eavesdropping via cognitive jamming in fading channels"

Connected through hyperedge HE-4 (induced by source paper "a deep learning approach to universal binary visible light communication transceiver"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "an efficient flicker free fec coding scheme for dimmable visible light communication based on polar codes"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "estimating or propagating gradients through stochastic neurons"

Connected through hyperedge HE-5 (induced by source paper "low complexity autoencoder based end to end learning of coded communications systems"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"

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)