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: "iroko a framework to prototype reinforcement learning for data center traffic control"
  Abstract: Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far render it unacceptable for use in datacenter networks. In this paper, we analyze the requirements for RL to succeed in the datacenter context. We present a new emulator, Iroko, which we developed to support different network topologies, congestion control algorithms, and deployment scenarios. Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fair evaluation of different RL and traditional CC algorithms under the same conditions. We present initial benchmarks on three deep RL algorithms compared to TCP New Vegas and DCTCP. Our results show that these algorithms are able to learn a CC policy which exceeds the performance of TCP New Vegas on a dumbbell and fat-tree topology. We make our emulator open-source and publicly available: this https URL

Connected through hyperedge HE-1 (induced by source paper "a view on deep reinforcement learning in system optimization"):
  with members:
    - "neural packet classification"
    - "neo a learned query optimizer"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "a survey on compiler autotuning using machine learning"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "configtron tackling network diversity with heterogeneous configurations"):
  with members:
    - "an empirical view on content provider fairness"
    - "beyond socket options making the linux tcp stack truly extensible"
    - "internet congestion control via deep reinforcement learning"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "raptor routing attacks on privacy in tor"
    - "pcc re architecting congestion control for consistent high performance"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning for bandwidth estimation and congestion control in real time communications"):
  with members:
    - "mvfst rl an asynchronous rl framework for congestion control with delayed actions"
    - "qflow a reinforcement learning approach to high qoe video streaming over wireless networks"
    - "continual learning improves internet video streaming"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "mvfst rl an asynchronous rl framework for congestion control with delayed actions"):
  with members:
    - "torchbeast a pytorch platform for distributed rl"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-5 (induced by source paper "internet congestion control via deep reinforcement learning"):
  with members:
    - "efficient gan based anomaly detection"
    - "grounding language for transfer in deep reinforcement learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "distance based confidence score for neural network classifiers"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "successor features for transfer in reinforcement learning"
    - "value iteration networks"

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)