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: "content level selective offloading in heterogeneous networks multi armed bandit optimization and regret bounds"
  Abstract: We consider content-level selective offloading of cellular downlink traffic to a wireless infostation terminal which stores high data-rate content in its cache memory. Cellular users in the vicinity of the infostation can directly download the stored content from the infostation through a broadband connection (e.g., WiFi), reducing the latency and load on the cellular network. The goal of the infostation cache controller (CC) is to store the most popular content in the cache memory such that the maximum amount of traffic is offloaded to the infostation. In practice, the popularity profile of the files is not known by the CC, which observes only the instantaneous demands for those contents stored in the cache. Hence, the cache content placement is optimised based on the demand history and on the cost associated to placing each content in the cache. By refreshing the cache content at regular time intervals, the CC gradually learns the popularity profile, while at the same time exploiting the limited cache capacity in the best way possible. This is formulated as a multi-armed bandit (MAB) problem with switching cost. Several algorithms are presented to decide on the cache content over time. The performance is measured in terms of cache efficiency, defined as the amount of net traffic that is offloaded to the infostation. In addition to theoretical regret bounds, the proposed algorithms are analysed through numerical simulations. In particular, the impact of system parameters, such as the number of files, number of users, cache size, and skewness of the popularity profile, on the performance is studied numerically. It is shown that the proposed algorithms learn the popularity profile quickly for a wide range of system parameters.

Connected through hyperedge HE-1 (induced by source paper "optimal and scalable caching for 5g using reinforcement learning of space time popularities"):
  with members:
    - "ultra dense edge caching under spatio temporal demand and network dynamics"
    - "optimal caching and scheduling for cache enabled d2d communications"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "wireless device to device caching networks basic principles and system performance"
    - "wireless caching technical misconceptions and business barriers"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "placing dynamic content in caches with small population"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "using grouped linear prediction and accelerated reinforcement learning for online content caching"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "dynamic mobile edge caching with location differentiation"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "continuous deep q learning with model based acceleration"
    - "a transfer learning approach for cache enabled wireless networks"
    - "learning based optimization of cache content in a small cell base station"
    - "living on the edge the role of proactive caching in 5g wireless networks"

Connected through hyperedge HE-3 (induced by source paper "context aware proactive content caching with service differentiation in wireless networks"):
  with members:
    - "a non stochastic learning approach to energy efficient mobility management"
    - "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"
    - "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-4 (induced by source paper "breaking the economic barrier of caching in cellular networks incentives and contracts"):
  with members:
    - "pricing and resource allocation via game theory for a small cell video caching system"
    - "caching incentive design in wireless d2d networks a stackelberg game approach"
    - "wireless caching technical misconceptions and business barriers"
    - "optimal geographic caching in cellular networks"
    - "fundamental limits of caching in wireless d2d networks"
    - "what will 5g be"

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.IT(Information Theory)