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 to predict where to look in interactive environments using deep recurrent q learning"
  Abstract: Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games). In this paper, we leverage Reinforcement Learning (RL) to highlight task-relevant locations of input frames. We propose a soft attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL agent how to play a game and where to look by focusing on the most pertinent parts of its visual input. Our evaluations on several Atari 2600 games show that the soft attention based model could predict fixation locations significantly better than bottom-up models such as Itti-Kochs saliency and Graph-Based Visual Saliency (GBVS) models.

Connected through hyperedge HE-1 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable reinforcement learning through a causal lens"
    - "atari head atari human eye tracking and demonstration dataset"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "free lunch saliency via attention in atari agents"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"

Connected through hyperedge HE-2 (induced by source paper "free lunch saliency via attention in atari agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "atari head atari human eye tracking and demonstration dataset"
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an introduction to deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "atari head atari human eye tracking and demonstration dataset"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "minerl a large scale dataset of minecraft demonstrations"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "free lunch saliency via attention in atari agents"
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "agil learning attention from human for visuomotor tasks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "wildfire monitoring in remote areas using autonomous unmanned aerial vehicles"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "autonomous extraction of a hierarchical structure of tasks in reinforcement learning a sequential associate rule mining approach"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "on greedy routing in dynamic uav networks"
    - "a unified framework for joint mobility prediction and object profiling of drones in uav networks"

Connected through hyperedge HE-5 (induced by source paper "an autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations"):
  with members:
    - "a solution for dynamic spectrum management in mission critical uav networks"
    - "a survey on hardware based security mechanisms for internet of things"
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "waveform and spectrum management for unmanned aerial systems beyond 2025"

Connected through hyperedge HE-6 (induced by source paper "a solution for dynamic spectrum management in mission critical uav networks"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "on greedy routing in dynamic uav networks"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-7 (induced by source paper "agil learning attention from human for visuomotor tasks"):
  with members:
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "deep q learning from demonstrations"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "eye tracking for everyone"
    - "what do different evaluation metrics tell us about saliency models"

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.CV(Computer Vision and Pattern Recognition)