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: "learning based task offloading for vehicular cloud computing systems"
  Abstract: Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrence-awareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to-optimal delay performance.

Connected through hyperedge HE-1 (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"
    - "a survey on mobile edge computing the communication perspective"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive learning based task offloading for vehicular edge computing systems"):
  with members:
    - "adaptive exploration exploitation tradeoff for opportunistic bandits"
    - "a survey on mobile edge computing the communication perspective"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"
    - "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-3 (induced by source paper "exploiting moving intelligence delay optimized computation offloading in vehicular fog networks"):
  with members:
    - "coding for distributed fog computing"
    - "a survey on mobile edge computing the communication perspective"
    - "towards low latency and ultra reliable vehicle to vehicle communication"
    - "task replication for deadline constrained vehicular cloud computing optimal policy performance analysis and implications on road traffic"
    - "effect of user mobility on the performance of device to device networks with distributed caching"
    - "effect of retransmissions on optimal caching in cache enabled 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)