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: "downlink performance of cellular systems with base station sleeping user association and scheduling"
  Abstract: Base station (BS) sleeping has emerged as a viable solution to enhance the overall network energy efficiency by inactivating the underutilized BSs. However, it affects the performance of users in sleeping cells depending on their BS association criteria, their channel conditions toward the active BSs, and scheduling criteria and traffic loads at the active BSs. This paper characterizes the performance of cellular systems with BS sleeping by developing a systematic framework to derive the spectral efficiency and outage probability of downlink transmission to the sleeping cell users taking into account the aforementioned factors. In this context, a user association scheme is also developed in which sleeping cell users associate to a BS with maximum mean channel access probability (MMAP). The MMAP-based user association scheme adapts according to traffic load and scheduling criteria at the active BSs. We consider greedy and round-robin schemes at active BSs for scheduling users in a channel. We also derive the analytical results for the conventional maximum received signal power (MRSP)-based user association scheme. Finally, we derive the statistics of the received signal and interference power to evaluate the downlink spectral efficiency of a given sleeping cell user. Numerical results provide a comparison between two user association schemes as a function of system parameters and demonstrate the efficacy of the MMAP-based association scheme in non-uniform traffic load scenarios.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication"):
  with members:
    - "vehicular edge computing via deep reinforcement learning"
    - "wireless edge computing with latency and reliability guarantees"
    - "learning to optimize training deep neural networks for interference management"
    - "federated learning for ultra reliable low latency v2v communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "user association and load balancing for massive mimo through deep learning"
    - "load balancing user association in millimeter wave mimo networks"

Connected through hyperedge HE-2 (induced by source paper "energy efficient mechanism for smart communication in cellular networks"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"
    - "exploiting network cooperation in green wireless communication"
    - "transmission capacity of wireless networks"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "a survey of green networking research"

Connected through hyperedge HE-3 (induced by source paper "energy sustainable paradigms and methods for future mobile networks a survey"):
  with members:
    - "information exchange in randomly deployed dense wsns with wireless energy harvesting capabilities"
    - "a stochastic geometry based demand response management framework for cellular networks powered by smart grid"
    - "fog computing based radio access networks issues and challenges"
    - "a hybrid energy sharing framework for green cellular networks"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "green networking in cellular hetnets a unified radio resource management framework with base station on off switching"
    - "online ski rental for on off scheduling of energy harvesting base stations"
    - "dynamic clustering and on off strategies for wireless small cell networks"

Connected through hyperedge HE-4 (induced by source paper "drag deep reinforcement learning based base station activation in heterogeneous networks"):
  with members:
    - "parameter space noise for exploration"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "deep reinforcement learning in large discrete action spaces"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep reinforcement learning in parameterized action space"
    - "deep reinforcement learning with double q learning"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "base station switch off with mutual repulsion in fifth generation massive multi input multi output networks"):
  with members:
    - "toward a tractable delay analysis in ultra dense networks"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "5g ultra dense cellular networks"
    - "how many small cells can be turned off via vertical offloading under a separation architecture"

Connected through hyperedge HE-6 (induced by source paper "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"):
  with members:
    - "backhaul aware user association and resource allocation for energy constrained hetnets"
    - "distributed association and relaying with fairness in millimeter wave networks"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "user association and interference management in massive mimo hetnets"
    - "efficient scheduling and power allocation for d2d assisted wireless caching networks"
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"
    - "matching theory for backhaul management in small cell networks with mmwave capabilities"
    - "an overview of load balancing in hetnets old myths and open problems"

Connected through hyperedge HE-7 (induced by source paper "a non stationary service curve model for estimation of cellular sleep scheduling"):
  with members:
    - "a non stationary service curve model for performance analysis of transient phases"
    - "delay bounds in communication networks with heavy tailed and self similar traffic"
    - "a system theoretic approach to bandwidth estimation"
    - "an end to end probabilistic network calculus with moment generating functions"

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)