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: "experience driven networking a deep reinforcement learning based approach"
  Abstract: Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based control framework, DRL-TE, for a fundamental networking problem: Traffic Engineering (TE). The proposed framework maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful Deep Neural Networks (DNNs). We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. To validate and evaluate the proposed framework, we implemented it in ns-3, and tested it comprehensively with both representative and randomly generated network topologies. Extensive packet-level simulation results show that 1) compared to several widely-used baseline methods, DRL-TE significantly reduces end-to-end delay and consistently improves the network utility, while offering better or comparable throughput; 2) DRL-TE is robust to network changes; and 3) DRL-TE consistently outperforms a state-ofthe-art DRL method (for continuous control), Deep Deterministic Policy Gradient (DDPG), which, however, does not offer satisfying performance.

Connected through hyperedge HE-1 (induced by source paper "taurus an intelligent data plane"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "meta learning neural bloom filters"
    - "fpga based accelerators of deep learning networks for learning and classification a review"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "in network neural networks"
    - "structured control nets for deep reinforcement learning"
    - "training deep neural networks with 8 bit floating point numbers"
    - "how many mice make an elephant modelling flow length and size distribution of internet traffic"

Connected through hyperedge HE-2 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning"):
  with members:
    - "ieee 802 11be extremely high throughput the next generation of wi fi technology beyond 802 11ax"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "deep reinforcement learning for resource management in network slicing"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "progressive neural architecture search"
    - "hindsight experience replay"
    - "a deep reinforcement learning based framework for content caching"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning meets graph neural networks exploring a routing optimization use case"):
  with members:
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"
    - "understanding the modeling of computer network delays using neural networks"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "interaction networks for learning about objects relations and physics"
    - "playing atari with deep reinforcement learning"
    - "fast emergency paths schema to overcome transient link failures in ospf routing"

Connected through hyperedge HE-4 (induced by source paper "dl2 a deep learning driven scheduler for deep learning clusters"):
  with members:
    - "online job scheduling in distributed machine learning clusters"
    - "learning scheduling algorithms for data processing clusters"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "slaq quality driven scheduling for distributed machine learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "device placement optimization with reinforcement learning"
    - "towards distributed machine learning in shared clusters a dynamically partitioned approach"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-5 (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"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a deep reinforcement learning approach for software defined networking routing optimization"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-6 (induced by source paper "routenet leveraging graph neural networks for network modeling and optimization in sdn"):
  with members:
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"
    - "relational inductive biases deep learning and graph networks"
    - "machine learning for networking workflow advances and opportunities"
    - "understanding the modeling of computer network delays using neural networks"
    - "knowledge defined networking"
    - "self normalizing neural networks"
    - "neural message passing for quantum chemistry"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-7 (induced by source paper "unveiling the potential of graph neural networks for network modeling and optimization in sdn"):
  with members:
    - "message passing neural networks learn little s law"
    - "relational inductive biases deep learning and graph networks"
    - "machine learning for networking workflow advances and opportunities"
    - "understanding the modeling of computer network delays using neural networks"
    - "knowledge defined networking"
    - "self normalizing neural networks"
    - "neural message passing for quantum chemistry"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "vehicular edge computing via deep reinforcement learning"):
  with members:
    - "decorrelation of neutral vector variables theory and applications"
    - "optimal auction for edge computing resource management in mobile blockchain networks a deep learning approach"
    - "the role of data analysis in the development of intelligent energy networks"
    - "efficient multi user computation offloading for mobile edge cloud computing"

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)