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: "big data meets telcos a proactive caching perspective"
  Abstract: Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.

Connected through hyperedge HE-1 (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale"):
  with members:
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "network slicing for ultra reliable low latency communication in industry 4 0 scenarios"
    - "proactive edge computing in latency constrained fog networks"
    - "fine grained reliability for v2v communications around suburban and urban intersections"
    - "introduction to ultra reliable and low latency communications in 5g"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "joint caching and pricing strategies for popular content in information centric networks"):
  with members:
    - "echo state networks for proactive caching in cloud based radio access networks with mobile users"
    - "wireless device to device caching networks basic principles and system performance"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "a stackelberg game for incentive proactive caching mechanisms in wireless networks"
    - "octopus a cooperative hierarchical caching strategy for cloud radio access networks"
    - "collaborative multi bitrate video caching and processing in mobile edge computing networks"
    - "cooperative hierarchical caching in 5g cloud radio access networks c rans"
    - "mean field games for distributed caching in ultra dense small cell 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)