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: "spatio temporal edge service placement a bandit learning approach"
  Abstract: Shared edge computing platforms deployed at the radio access network are expected to significantly improve quality of service delivered by Application Service Providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP's prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is "combinatorial" because only a limited number of edge sites can be rented to provide the edge service given the ASP's budget. It is "contextual" because we utilize user context information to enable finer-grained learning and decision making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared to an oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions.

Connected through hyperedge HE-1 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-2 (induced by source paper "joint optimization of service caching placement and computation offloading in mobile edge computing system"):
  with members:
    - "joint service placement and request routing in multi cell mobile edge computing networks"
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "price based distributed offloading for mobile edge computing with computation capacity constraints"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for cache assisted mobile edge computing"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "live prefetching for mobile computation offloading"

Connected through hyperedge HE-3 (induced by source paper "budget constrained edge service provisioning with demand estimation via bandit learning"):
  with members:
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "a survey on mobile edge computing the communication perspective"
    - "computation peer offloading for energy constrained mobile edge computing in small cell networks"
    - "socially trusted collaborative edge computing in ultra dense networks"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "adaptive ensemble learning with confidence bounds"
    - "distributed online learning via cooperative contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "distributed redundancy scheduling for microservice based applications at the edge"):
  with members:
    - "kubernetes as an availability manager for microservice applications"
    - "follow me at the edge mobility aware dynamic service placement for mobile edge computing"
    - "online job scheduling with redundancy and opportunistic checkpointing a speedup function based analysis"
    - "a survey on mobile edge computing the communication perspective"

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)