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: "redesigning fronthaul for next generation networks beyond baseband samples and point to point links"
  Abstract: The fronthaul is an indispensable enabler for 5G networks. However, the classical fronthauling method demands large bandwidth, low latency, and tight synchronization from the transport network, and only allows for point-to-point logical topology. This greatly limits the usage of fronthaul in many 5G scenarios. In this article, we introduce a new perspective to understand and design fronthaul for next-generation wireless access. We allow the renovated fronthaul to transport information other than time-domain I/Q samples and to support logical topologies beyond point-to-point links. In this way, different function splitting schemes can be incorporated into the radio access network to satisfy the bandwidth and latency requirements of ultradense networks, control/data decoupling architectures, and delay-sensitive communications. At the same time, massive cooperation and devicecentric networking could be effectively enabled with point-to-multipoint fronthaul transportation. We analyze three unique design requirements for the renovated fronthaul, including the ability to handle various payload traffic, support different logical topology, and provide differentiated latency guarantee. Following this analysis, we propose a reference architecture for designing the renovated fronthaul. The required functionalities are categorized into four logical layers and realized using novel technologies such as decoupled synchronization layer, packet switching, and session-based control. We also discuss some important future research issues.

Connected through hyperedge HE-1 (induced by source paper "flexible functional split and power control for energy harvesting cloud radio access networks"):
  with members:
    - "optimal placement of baseband functions for energy harvesting virtual small cells"
    - "on the fronthaul statistical multiplexing gain"
    - "online ski rental for on off scheduling of energy harvesting base stations"
    - "greendelivery proactive content caching and push with energy harvesting based small cells"
    - "5g wireless backhaul networks challenges and research advances"
    - "energy harvesting broadband communication systems with processing energy cost"
    - "optimal power allocation for energy harvesting and power grid coexisting wireless communication systems"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-2 (induced by source paper "on the fronthaul statistical multiplexing gain"):
  with members:
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-3 (induced by source paper "tradeoff caching strategy of outage probability and fronthaul usage in cloud ran"):
  with members:
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "cache enabled heterogeneous cellular networks optimal tier level content placement"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "distributed edge caching scheme considering the tradeoff between the diversity and redundancy of cached content"
    - "backhaul aware caching placement for wireless networks"
    - "content centric multicast beamforming in cache enabled cloud radio access networks"
    - "base station assisted device to device communications for high throughput wireless video networks"
    - "fundamental limits of caching"

Connected through hyperedge HE-4 (induced by source paper "statistical multiplexing gain analysis of heterogeneous virtual base station pools in cloud radio access networks"):
  with members:
    - "software defined hyper cellular architecture for green and elastic wireless access"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "on the statistical multiplexing gain of virtual base station pools"
    - "concert a cloud based architecture for next generation cellular systems"

Connected through hyperedge HE-5 (induced by source paper "software defined hyper cellular architecture for green and elastic wireless access"):
  with members:
    - "hycell enabling green base station operations in software defined radio access networks"
    - "graph based framework for flexible baseband function splitting and placement in c ran"
    - "concert a cloud based architecture for next generation cellular systems"

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)