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: "deep reinforcement learning based resource allocation for v2v communications"
  Abstract: In this paper, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communications based on deep reinforcement learning, which can be applied to both unicast and broadcast scenarios. According to the decentralized resource allocation mechanism, an autonomous agent', a V2V link or a vehicle, makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Since the proposed method is decentralized, it incurs only limited transmission overhead. From the simulation results, each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) communications.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based wireless resource allocation with application to vehicular networks"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "multi agent learning for cooperative large scale caching networks"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "edge caching for coverage and capacity aided heterogeneous networks"

Connected through hyperedge HE-2 (induced by source paper "voting based multi agent reinforcement learning for intelligent iot"):
  with members:
    - "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-3 (induced by source paper "learning to branch accelerating resource allocation in wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "energy efficient joint resource allocation and power control for d2d communications"
    - "radio resource allocation for device to device underlay communication using hypergraph theory"
    - "a survey on device to device communication in cellular networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "fog computing based radio access networks issues and challenges"
    - "joint user association and resource allocation in virtualized wireless networks"

Connected through hyperedge HE-5 (induced by source paper "accelerating generalized benders decomposition for wireless resource allocation"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "learning to optimize training deep neural networks for interference management"
    - "energy efficient resource allocation for mobile edge computation offloading"

Connected through hyperedge HE-6 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-7 (induced by source paper "20 years of evolution from cognitive to intelligent communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning in physical layer communications"
    - "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-8 (induced by source paper "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "learning to entangle radio resources in vehicular communications an oblivious game theoretic perspective"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "deep learning with long short term memory for time series prediction"
    - "performance optimization in mobile edge computing via deep reinforcement learning"

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)