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 utility optimization approach to network cache design"
  Abstract: In any caching system, the admission and eviction policies determine which contents are added and removed from a cache when a miss occurs. Usually, these policies are devised so as to mitigate staleness and increase the hit probability. Nonetheless, the utility of having a high hit probability can vary across contents. This occurs, for instance, when service level agreements must be met, or if certain contents are more difficult to obtain than others. In this paper, we propose utility-driven caching, where we associate with each content a utility, which is a function of the corresponding content hit probability. We formulate optimization problems where the objectives are to maximize the sum of utilities over all contents. These problems differ according to the stringency of the cache capacity constraint. Our framework enables us to reverse engineer classical replacement policies such as LRU and FIFO, by computing the utility functions that they maximize. We also develop online algorithms that can be used by service providers to implement various caching policies based on arbitrary utility functions.

Connected through hyperedge HE-1 (induced by source paper "online convex optimization for caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "learning to cooperate in d2d caching networks"
    - "cache optimization models and algorithms"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-2 (induced by source paper "a ttl based approach for content placement in edge networks"):
  with members:
    - "cache version selection and content placement for adaptive video streaming in wireless edge networks"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "adaptive caching networks with optimality guarantees"
    - "on the complexity of optimal routing and content caching in heterogeneous networks"
    - "a unified approach to the performance analysis of caching systems"

Connected through hyperedge HE-3 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "how often should i access my online social networks"):
  with members:
    - "age of information vs value of information scheduling for cellular networked control systems"
    - "performance analysis of online social platforms"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "biases in the facebook news feed a case study on the italian elections"
    - "fairness in online social network timelines measurements models and mechanism design"
    - "age optimal sampling and transmission scheduling in multi source systems"
    - "optimal status update for age of information minimization with an energy harvesting source"
    - "characterizing information diets of social media users"

Connected through hyperedge HE-5 (induced by source paper "the role of caching in future communication systems and networks"):
  with members:
    - "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "on coding for cache aided delivery of dynamic correlated content"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "updating content in cache aided coded multicast"
    - "on the effects of subpacketization in content centric mobile networks"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"

Connected through hyperedge HE-6 (induced by source paper "fairness in online social network timelines measurements models and mechanism design"):
  with members:
    - "biases in the facebook news feed a case study on the italian elections"
    - "do diffusion protocols govern cascade growth"
    - "closing the ai knowledge gap"
    - "fairness of exposure in rankings"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "alpha fair coded caching"
    - "on fairness and calibration"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-7 (induced by source paper "caching with time domain buffer sharing"):
  with members:
    - "self sustaining caching stations toward cost effective 5g enabled vehicular networks"
    - "wireless device to device caching networks basic principles and system performance"
    - "big data caching for networking moving from cloud to edge"
    - "optimal storage allocation for wireless cloud caching systems with a limited sum storage capacity"
    - "improved approximation of storage rate tradeoff for caching with multiple demands"
    - "coded caching for multi level popularity and access"
    - "online coded caching"
    - "coded caching with nonuniform demands"

Connected through hyperedge HE-8 (induced by source paper "worst case bounds and optimized cache on mth request cache insertion policies under elastic conditions"):
  with members:
    - "caching in the clouds optimized dynamic cache instantiation in content delivery systems"
    - "practical bounds on optimal caching with variable object sizes"
    - "a unified approach to the performance analysis of caching systems"
    - "a versatile and accurate approximation for lru cache performance"

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)