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: "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
  Abstract: Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: 1) avoiding causing harmful interference to primary users (PUs) and 2) conducting effective interference coordination with other SUs. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn “appropriate” spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network, called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the  Q -learning method when the number of channels is large.

Connected through hyperedge HE-1 (induced by source paper "recent advances in physical reservoir computing a review"):
  with members:
    - "echo state networks are universal"
    - "reservoir computing hardware with cellular automata"
    - "scaling up echo state networks with multiple light scattering"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "reservoir computing universality with stochastic inputs"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "reservoir computing using non uniform binary cellular automata"
    - "computing with networks of nonlinear mechanical oscillators"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning with convolutional reservoir computing"):
  with members:
    - "effect of shapes of activation functions on predictability in the echo state network"
    - "weight agnostic neural networks"
    - "deep neuroevolution of recurrent and discrete world models"
    - "distributed prioritized experience replay"
    - "recurrent world models facilitate policy evolution"
    - "deep reinforcement learning a brief survey"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "non uniform time step deep q network for carrier sense multiple access in heterogeneous wireless networks"):
  with members:
    - "carrier sense multiple access for heterogeneous wireless networks using deep reinforcement learning"
    - "actor critic deep reinforcement learning for dynamic multichannel access"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning an overview"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"):
  with members:
    - "learning the mmse channel estimator"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "an introduction to deep learning for the physical layer"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-5 (induced by source paper "carrier sense multiple access for heterogeneous wireless networks using deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning an overview"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)