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: "machine learning in the air"
  Abstract: Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -- ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

Connected through hyperedge HE-1 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "multi resolution csi feedback with deep learning in massive mimo system"
    - "deep convolutional compression for massive mimo csi feedback"
    - "mimo channel information feedback using deep recurrent network"

Connected through hyperedge HE-2 (induced by source paper "federated over the air subspace learning from incomplete data"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "provable subspace tracking from missing data and matrix completion"
    - "federated learning over wireless fading channels"
    - "federated pca with adaptive rank estimation"
    - "federated machine learning concept and applications"
    - "byzantine stochastic gradient descent"
    - "federated learning via over the air computation"

Connected through hyperedge HE-3 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-4 (induced by source paper "energy efficient federated learning over wireless communication networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient radio resource allocation for federated edge learning"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "multi hop federated private data augmentation with sample compression"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-5 (induced by source paper "hierarchical federated learning across heterogeneous cellular networks"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "accelerating dnn training in wireless federated edge learning system"
    - "towards federated learning at scale system design"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "robust and communication efficient federated learning from non iid data"
    - "a distributed hierarchical sgd algorithm with sparse global reduction"
    - "edge assisted hierarchical federated learning with non iid data"

Connected through hyperedge HE-6 (induced by source paper "deep learning for distributed optimization applications to wireless resource management"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "learning to optimize training deep neural networks for interference management"
    - "end to end deep learning of optical fiber communications"
    - "deep learning framework for wireless systems applications to optical wireless 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-7 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "auto scaling network resources using machine learning to improve qos and reduce cost"
    - "addressing function approximation error in actor critic methods"
    - "deep learning in mobile and wireless networking a survey"
    - "comparative study of virtual machines and containers for devops developers"

Connected through hyperedge HE-8 (induced by source paper "machine intelligence at the edge with learning centric power allocation"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "a speculative study on 6g"
    - "the roadmap to 6g ai empowered wireless networks"
    - "wireless network intelligence at the edge"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "deep learning in mobile and wireless networking a survey"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"

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)