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 deep reinforcement learning approach for software defined networking routing optimization"
  Abstract: In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.

Connected through hyperedge HE-1 (induced by source paper "toward packet routing with fully distributed multi agent deep reinforcement learning"):
  with members:
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning a brief survey"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "hierarchical deep double q routing"):
  with members:
    - "toward packet routing with fully distributed multi agent deep reinforcement learning"
    - "learning multi agent communication under limited bandwidth restriction for internet packet routing"
    - "reward design in cooperative multi agent reinforcement learning for packet routing"
    - "experience driven networking a deep reinforcement learning based approach"
    - "rainbow combining improvements in deep reinforcement learning"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "hymer a hybrid machine learning framework for energy efficient routing in sdn"):
  with members:
    - "artificial intelligence enabled software defined networking a comprehensive overview"
    - "resdn a novel metric and method for energy efficient routing in software defined networks"
    - "neuroute predictive dynamic routing for software defined networks"
    - "seer empowering software defined networking with data analytics"
    - "software defined networking a comprehensive survey"

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.NI(Networking and Internet Architecture)