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: "emm energy aware mobility management for mobile edge computing in ultra dense networks"
  Abstract: Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.

Connected through hyperedge HE-1 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "on the energy and data storage management in energy harvesting wireless communications"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-2 (induced by source paper "age of information optimized mac in v2x sidelink via piggyback based collaboration"):
  with members:
    - "ai assisted low information latency wireless networking"
    - "analytical models of the performance of c v2x mode 4 vehicular communications"
    - "hierarchical soft slicing to meet multi dimensional qos demand in cache enabled vehicular networks"
    - "minimizing the age of information in a csma environment"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "study of the impact of phy and mac parameters in 3gpp c v2v mode 4"
    - "distributed scheduling algorithms for optimizing information freshness in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "distributed task replication for vehicular edge computing performance analysis and learning based algorithm"):
  with members:
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "exploiting moving intelligence delay optimized computation offloading in vehicular fog networks"
    - "heterogeneous coded computation across heterogeneous workers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "dense moving fog for intelligent iot key challenges and opportunities"
    - "learning based task offloading for vehicular cloud computing systems"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-4 (induced by source paper "workload scheduling on heterogeneous mobile edge cloud in 5g networks to minimize sla violation"):
  with members:
    - "quantitative overfitting management for human in the loop ml application development with ease ml meter"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-5 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "are fpgas suitable for edge computing"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "fog computing a taxonomy survey and future directions"
    - "data analytics service composition and deployment on edge devices"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"

Connected through hyperedge HE-6 (induced by source paper "mobile edge intelligence and computing for the internet of vehicles"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "client edge cloud hierarchical federated learning"
    - "a mobility aware vehicular caching scheme in content centric networks model and optimization"
    - "edge assisted hierarchical federated learning with non iid data"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "federated learning strategies for improving communication efficiency"
    - "exploiting mobility in cache assisted d2d networks performance analysis and optimization"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

Connected through hyperedge HE-7 (induced by source paper "adaptive learning based task offloading for vehicular edge computing systems"):
  with members:
    - "learning based task offloading for vehicular cloud computing systems"
    - "adaptive exploration exploitation tradeoff for opportunistic bandits"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "task replication for deadline constrained vehicular cloud computing optimal policy performance analysis and implications on road traffic"
    - "a non stochastic learning approach to energy efficient mobility management"
    - "vehicle to vehicle communications with urban intersection path loss models"
    - "energy efficient resource allocation for mobile edge computation offloading"

Connected through hyperedge HE-8 (induced by source paper "survey on multi access edge computing for internet of things realization"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "knowledge defined networking"
    - "hyperprofile based computation offloading for mobile edge networks"
    - "a survey on mobile edge computing the communication perspective"
    - "realizing icn in 3gpp s 5g nextgen core architecture"
    - "virtual reality over wireless networks quality of service model and learning based resource management"

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)