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 robust machine learning method for cell load approximation in wireless networks"
  Abstract: We propose a learning algorithm for cell-load approximation in wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty arising from a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales because of a fast time-varying communication environment. The first part of this work studies the set of feasible rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the unique fixed point of the non-linear cell-load mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework that achieves the best worst-case performance. Furthermore, the approximation preserves the monotonicity and continuity properties. Simulations show that the proposed method exhibits better robustness and accuracy for small training sets in comparison with standard approximation techniques for multivariate data.

Connected through hyperedge HE-1 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-2 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-3 (induced by source paper "a note on decoding order in optimizing multi cell noma"):
  with members:
    - "a framework for optimizing multi cell noma delivering demand with less resource"
    - "resource optimization with load coupling in multi cell noma"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"
    - "energy aware wireless relay selection in load coupled ofdma cellular networks"
    - "load optimization with user association in cooperative and load coupled lte networks"
    - "dynamic user clustering and power allocation for uplink and downlink non orthogonal multiple access noma systems"
    - "elementary properties of positive concave mappings with applications to network planning and optimization"
    - "power and channel allocation for non orthogonal multiple access in 5g systems tractability and computation"

Connected through hyperedge HE-4 (induced by source paper "statistical learning of geometric characteristics of wireless networks"):
  with members:
    - "super resolution with deep convolutional sufficient statistics"
    - "how user throughput depends on the traffic demand in large cellular networks"
    - "analysis of cell load coupling for lte network planning and optimization"

Connected through hyperedge HE-5 (induced by source paper "resource optimization with load coupling in multi cell noma"):
  with members:
    - "a framework for optimizing multi cell noma delivering demand with less resource"
    - "on optimal power allocation for downlink non orthogonal multiple access systems"
    - "energy aware wireless relay selection in load coupled ofdma cellular networks"
    - "sub channel assignment power allocation and user scheduling for non orthogonal multiple access networks"
    - "load optimization with user association in cooperative and load coupled lte networks"
    - "elementary properties of positive concave mappings with applications to network planning and optimization"
    - "power and channel allocation for non orthogonal multiple access in 5g systems tractability and computation"
    - "energy efficiency of distributed signal processing in wireless networks a cross layer analysis"

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)