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: "pcc re architecting congestion control for consistent high performance"
  Abstract: TCP and its variants have suffered from surprisingly poor performance for decades. We argue the TCP family has little hope to achieve consistent high performance due to a fundamental architectural deficiency: hardwiring packet-level events to control responses without understanding the real performance result of its actions. We propose Performance-oriented Congestion Control (PCC), a new congestion control architecture in which each sender continuously observes the connection between its actions and empirically experienced performance, enabling it to consistently adopt actions that result in high performance. We prove that PCC converges to a stable and fair equilibrium. Across many real-world and challenging environments, PCC shows consistent and often 10x performance improvement, with better fairness and stability than TCP. PCC requires no router hardware support or new packet format.

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"
    - "experience driven networking a deep reinforcement learning based approach"
    - "in network neural networks"
    - "structured control nets for deep reinforcement learning"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-2 (induced by source paper "pbe cc congestion control via endpoint centric physical layer bandwidth measurements"):
  with members:
    - "abc a simple explicit congestion control protocol for wireless networks"
    - "fairness of congestion based congestion control experimental evaluation and analysis"
    - "owl a reliable online watcher for lte control channel measurements"
    - "srslte an open source platform for lte evolution and experimentation"

Connected through hyperedge HE-3 (induced by source paper "a survey on recent advances in transport layer protocols"):
  with members:
    - "a quic implementation for ns 3"
    - "beyond socket options making the linux tcp stack truly extensible"
    - "low delay scheduling of objects over multiple wireless paths"
    - "elasticity detection a building block for delay sensitive congestion control"
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "qaware a cross layer approach to mptcp scheduling"
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "machine learning for networking workflow advances and opportunities"

Connected through hyperedge HE-4 (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"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "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"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "the coco beholder enabling comprehensive evaluation of congestion control algorithms"):
  with members:
    - "fifty shades of congestion control a performance and interactions evaluation"
    - "an evaluation of bbr and its variants"
    - "fairness of congestion based congestion control experimental evaluation and analysis"
    - "congestion avoidance in computer networks with a connectionless network layer"

Connected through hyperedge HE-6 (induced by source paper "an evaluation of bbr and its variants"):
  with members:
    - "fifty shades of congestion control a performance and interactions evaluation"
    - "fairness of congestion based congestion control experimental evaluation and analysis"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-7 (induced by source paper "tiny buffer tcp for data center networks"):
  with members:
    - "homa a receiver driven low latency transport protocol using network priorities"
    - "finishing flows quickly with preemptive scheduling"

Connected through hyperedge HE-8 (induced by source paper "internet congestion control via deep reinforcement learning"):
  with members:
    - "efficient gan based anomaly detection"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "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"

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)