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: "radio resource allocation for reliable out of coverage v2v communications"
  Abstract: We explore a new approach to radio resource allocation for vehicle-to-vehicle (V2V) communications in case of out-of-coverage areas that are delimited by network infrastructure. By collecting and predicting information such as vehicle velocity, density and message traffic, the network infrastructure ensures reliability of the V2V services. We propose reserving required amount of resources for services that cannot be pre-scheduled (e.g., emergency braking, crash notifications, etc.), and scheduling those services that can be pre-scheduled (e.g., platooning). We analyze the resource reservation as a function of target reliability under varying vehicle densities and sizes of out-of-coverage area. For pre-scheduled services, we explore how variations in the vehicle velocities and predictions affect successful transmissions. The results indicate that increase in required reliability does not penalize the system prohibitively. On the other hand, speed prediction errors decrease the transmission success rate considerably, thus calling for a more flexible scheduler design.

Connected through hyperedge HE-1 (induced by source paper "a survey on resource allocation in vehicular networks"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "deep reinforcement learning a brief survey"
    - "vehicular communications a network layer perspective"
    - "an end to end network slicing framework for 5g wireless communication systems"
    - "reinforcement learning for resource provisioning in the vehicular cloud"
    - "coding based data broadcasting for time critical applications with rate adaptation"
    - "dynamic resource allocation in cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "vrls a unified reinforcement learning scheduler for vehicle to vehicle communications"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "transfer learning for related reinforcement learning tasks via image to image translation"
    - "reinforcement learning scheduler for vehicle to vehicle communications outside coverage"
    - "asynchronous methods for deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning scheduler for vehicle to vehicle communications outside coverage"):
  with members:
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "asynchronous methods for deep reinforcement learning"
    - "reinforcement learning for resource provisioning in the vehicular cloud"

Connected through hyperedge HE-4 (induced by source paper "reducing message collisions in sensing based semi persistent scheduling sps by using reselection lookaheads in cellular v2x"):
  with members:
    - "study of the impact of phy and mac parameters in 3gpp c v2v mode 4"
    - "a fixed point model for semi persistent scheduling of vehicular safety messages"
    - "performance analysis of sensing based semi persistent scheduling in c v2x 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.NI(Networking and Internet Architecture)