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 non stochastic learning approach to energy efficient mobility management"
  Abstract: Energy efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization-based mobility protocols cannot achieve long-term optimal energy consumption, particularly for ultra-dense networks (UDN). To address the complex dynamics of UDN, we propose a non-stochastic online-learning approach which does not make any assumption on the statistical behavior of the small base station (SBS) activities. In addition, we introduce handover cost to the overall energy consumption, which forces the resulting solution to explicitly minimize frequent handovers. The proposed Batched Randomization with Exponential Weighting (BREW) algorithm relies on batching to explore in bulk, and hence reduces unnecessary handovers. We prove that the regret of BREW is sublinear in time, thus guaranteeing its convergence to the optimal SBS selection. We further study the robustness of the BREW algorithm to delayed or missing feedback. Moreover, we study the setting where SBSs can be dynamically turned on and off. We prove that sublinear regret is impossible with respect to arbitrary SBS on/off, and then develop a novel learning strategy, called ranking expert (RE), that simultaneously takes into account the handover cost and the availability of SBS. To address the high complexity of RE, we propose a contextual ranking expert (CRE) algorithm that only assigns experts in a given context. Rigorous regret bounds are proved for both RE and CRE with respect to the best expert. Simulations show that not only do the proposed mobility algorithms greatly reduce the system energy consumption, but they are also robust to various dynamics which are common in practical ultra-dense wireless networks.

Connected through hyperedge HE-1 (induced by source paper "adaptive learning based task offloading for vehicular edge computing systems"):
  with members:
    - "learning based task offloading for vehicular cloud computing systems"
    - "adaptive exploration exploitation tradeoff for opportunistic bandits"
    - "a survey on mobile edge computing the communication perspective"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "task replication for deadline constrained vehicular cloud computing optimal policy performance analysis and implications on road traffic"
    - "vehicle to vehicle communications with urban intersection path loss models"
    - "energy efficient resource allocation for mobile edge computation offloading"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"):
  with members:
    - "cooperative ambient backscatter communications for green internet of things"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "spectrum access in cognitive radio using a two stage reinforcement learning approach"
    - "spectrum sharing for internet of things a survey"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-3 (induced by source paper "follow me at the edge mobility aware dynamic service placement for mobile edge computing"):
  with members:
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "efficient resource allocation for on demand mobile edge cloud computing"
    - "exploiting massive d2d collaboration for energy efficient mobile edge computing"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "toward hierarchical mobile edge computing an auction based profit maximization approach"
    - "dynamic service placement for mobile micro clouds with predicted future costs"
    - "dynamic service migration in mobile edge clouds"

Connected through hyperedge HE-4 (induced by source paper "context aware proactive content caching with service differentiation in wireless networks"):
  with members:
    - "multi armed bandits with application to 5g small cells"
    - "big data meets telcos a proactive caching perspective"
    - "distributed online learning in social recommender systems"
    - "learning based optimization of cache content in a small cell base station"
    - "content level selective offloading in heterogeneous networks multi armed bandit optimization and regret bounds"
    - "cache enabled small cell networks modeling and tradeoffs"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "content aware user clustering and caching in wireless small cell networks"

Connected through hyperedge HE-5 (induced by source paper "emm energy aware mobility management for mobile edge computing in ultra dense networks"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "user centric mobility management in ultra dense cellular networks under spatio temporal dynamics"
    - "how many small cells can be turned off via vertical offloading under a separation architecture"
    - "efficient multi user computation offloading for mobile edge cloud computing"
    - "mobility induced service migration in mobile micro clouds"
    - "five disruptive technology directions for 5g"

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)