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 machine learning approach for power allocation in hetnets considering qos"
  Abstract: There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets). However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problem in such complex networks. By defining each base station as an agent, a cellular network is modelled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network. In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.

Connected through hyperedge HE-1 (induced by source paper "power allocation in multi user cellular networks deep reinforcement learning approaches"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "fractional programming for communication systems part i power control and beamforming"
    - "power allocation in multi user cellular networks with deep q learning approach"
    - "an introduction to deep learning for the physical layer"
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep reinforcement learning an overview"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "lens based millimeter wave reconfigurable antenna noma"):
  with members:
    - "a new reconfigurable antenna mimo architecture for mmwave communication"
    - "reconfigurable antenna multiple access for 5g mmwave systems"
    - "impact of beam misalignment on hybrid beamforming noma for mmwave communications"
    - "optimal user scheduling and power allocation for millimeter wave noma systems"
    - "noma principles and recent results"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "5g ultra dense cellular networks"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for self organization and power control of two tier heterogeneous networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "from 4g to 5g self organized network management meets machine learning"
    - "what will 5g be"
    - "distributed cooperative q learning for power allocation in cognitive femtocell networks"
    - "power control in two tier femtocell networks"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "power allocation in multi user cellular networks with deep q learning approach"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning in physical layer communications"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "adam a method for stochastic optimization"

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)