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: "caching policy for cache enabled d2d communications by learning user preference"
  Abstract: Prior works in designing caching policy do not distinguish content popularity with user preference. In this paper, we illustrate the caching gain by exploiting individual user behavior in sending requests. After showing the connection between the two concepts, we provide a model for synthesizing user preference from content popularity. We then optimize the caching policy with the knowledge of user preference and active level to maximize the offloading probability for cache-enabled device-to-device communications, and develop a low-complexity algorithm to find the solution. In order to learn user preference, we model the user request behavior resorting to probabilistic latent semantic analysis, and learn the model parameters by expectation maximization algorithm. By analyzing a Movielens dataset, we find that the user preferences are less similar, and the active level and topic preference of each user change slowly over time. Based on this observation, we introduce a prior knowledge based learning algorithm for user preference, which can shorten the learning time. Simulation results show remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic dataset and synthetic data validated by the real dataset.

Connected through hyperedge HE-1 (induced by source paper "collaborative multi agent multi armed bandit learning for small cell caching"):
  with members:
    - "content popularity prediction towards location aware mobile edge caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "a deep reinforcement learning based framework for content caching"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching placement in stochastic wireless caching helper networks channel selection diversity via caching"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "learning to code coded caching via deep reinforcement learning"):
  with members:
    - "an approximation algorithm for optimal clique cover delivery in coded caching"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "deep learning based caching for self driving car in multi access edge computing"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "caching in combination networks novel multicast message generation and delivery by leveraging the network topology"
    - "fundamental tradeoff between storage and latency in cache aided wireless interference networks"
    - "a deep reinforcement learning based framework for content caching"
    - "multi antenna coded caching"

Connected through hyperedge HE-3 (induced by source paper "a personalized preference learning framework for caching in mobile networks"):
  with members:
    - "caching at base stations with heterogeneous user demands and spatial locality"
    - "user preference learning based edge caching for fog radio access network"
    - "on the effects of subpacketization in content centric mobile networks"
    - "how to cache in mobile hybrid iot networks"
    - "pricing and resource allocation via game theory for a small cell video caching system"
    - "information theoretic caching the multi user case"
    - "caching incentive design in wireless d2d networks a stackelberg game approach"
    - "a learning based approach to caching in heterogenous small cell networks"

Connected through hyperedge HE-4 (induced by source paper "optimal caching designs for perfect imperfect and unknown file popularity distributions in large scale multi tier wireless networks"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "a deep reinforcement learning based framework for content caching"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "optimization and analysis of probabilistic caching in n tier heterogeneous networks"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "placing dynamic content in caches with small population"

Connected through hyperedge HE-5 (induced by source paper "mobility aware content preference learning in decentralized caching networks"):
  with members:
    - "decentralized multi task learning based on extreme learning machines"
    - "user preference learning based edge caching for fog radio access network"
    - "exploiting mobility in cache assisted d2d networks performance analysis and optimization"
    - "learning to reweight examples for robust deep learning"
    - "mobility and popularity aware coded small cell caching"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "optimizing content caching to maximize the density of successful receptions in device to device networking"
    - "effect of user mobility on the performance of device to device networks with distributed caching"

Connected through hyperedge HE-6 (induced by source paper "learning to cooperate in d2d caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "a low complexity approach to distributed cooperative caching with geographic constraints"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "wireless caching technical misconceptions and business barriers"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "efficient and fair collaborative mobile internet access"

Connected through hyperedge HE-7 (induced by source paper "caching with time varying popularity profiles a learning theoretic perspective"):
  with members:
    - "ultra dense edge caching under spatio temporal demand and network dynamics"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "wireless content caching for small cell and d2d networks"
    - "a transfer learning approach for cache enabled wireless networks"
    - "efficient scheduling and power allocation for d2d assisted wireless caching networks"
    - "learning based optimization of cache content in a small cell base station"
    - "fundamental limits of caching in wireless d2d networks"

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)