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: "heterogeneous cellular networks with los and nlos transmissions the role of massive mimo and small cells"
  Abstract: We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Using stochastic geometry, we derive tight approximation of achievable downlink rate that enables us to compare the performance between densifying small cells and expanding BS antenna arrays. Interestingly, we find that adding small cells into the network improves the achievable rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spacial densification will lose its benefits and further impair the network capacity. To this end, we present the optimal small cell density that maximizes the rate as practical deployment guidance. In contrast, expanding macro BS antenna array can always benefit the capacity until an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from deployment of small cells. Furthermore, we find that allocating part of antennas to distributed small cell BSs works better than centralizing all antennas at the macro BS, and the optimal allocation proportion is also given for practical configuration reference. In summary, this work provides a further understanding on how to leverage small cells and massive MIMO in future heterogeneous cellular networks deployment.

Connected through hyperedge HE-1 (induced by source paper "mobile edge computing enabled heterogeneous networks"):
  with members:
    - "sir coverage analysis in cellular networks with temporal traffic a stochastic geometry approach"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "a survey on mobile edge computing the communication perspective"
    - "wireless networks for mobile edge computing spatial modeling and latency analysis extended version"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "latency and reliability aware task offloading and resource allocation for mobile edge computing"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "delay optimal computation task scheduling for mobile edge computing systems"

Connected through hyperedge HE-2 (induced by source paper "what is the optimal network deployment for a fixed density of antennas"):
  with members:
    - "on the performance of practical ultra dense networks the major and minor factors"
    - "study on the idle mode capability with los and nlos transmissions"
    - "what is the true value of dynamic tdd a mac layer perspective"
    - "please lower small cell antenna heights in 5g"
    - "performance impact of idle mode capability on dense small cell networks"
    - "performance impact of idle mode capability on dense small cell networks with los and nlos transmissions"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "massive mimo ten myths and one critical question"

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)