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 for resource allocation in v2v communications"
  Abstract: In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning. Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission. Since the proposed method is decentralized, the global information is not required for each agent to make its decisions, hence the transmission overhead is small. From the simulation results, each agent can learn how to satisfy the V2V constraints while minimizing the interference to vehicle-to-infrastructure (V2I) communications.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "federated learning strategies for improving communication efficiency"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "network slicing in fog radio access networks issues and challenges"
    - "communication efficient learning of deep networks from decentralized data"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning and adaptive sampling for optimized dnn compilation"):
  with members:
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "efficient neural architecture search via parameter sharing"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-4 (induced by source paper "accelerating dnn training in wireless federated edge learning system"):
  with members:
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "on device federated learning via blockchain and its latency analysis"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"

Connected through hyperedge HE-5 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint user association and resource allocation optimization for ultra reliable low latency hetnets"
    - "federated learning for ultra reliable low latency v2v communications"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"

Connected through hyperedge HE-6 (induced by source paper "toward intelligent vehicular networks a machine learning framework"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "machine learning for vehicular networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "vehicular communications a network layer perspective"
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-7 (induced by source paper "a deep actor critic reinforcement learning framework for dynamic multichannel access"):
  with members:
    - "reinforcement learning based resource allocation in fog radio access networks for various iot environments"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "heuristic search value iteration for pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"

Connected through hyperedge HE-8 (induced by source paper "a survey on resource allocation in vehicular networks"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "radio resource allocation for reliable out of coverage 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"

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)