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: "collaborative deep reinforcement learning for joint object search"
  Abstract: We examine the problem of joint top-down active search of multiple objects under interaction, e.g., person riding a bicycle, cups held by the table, etc.. Such objects under interaction often can provide contextual cues to each other to facilitate more efficient search. By treating each detector as an agent, we present the first collaborative multi-agent deep reinforcement learning algorithm to learn the optimal policy for joint active object localization, which effectively exploits such beneficial contextual information. We learn inter-agent communication through cross connections with gates between the Q-networks, which is facilitated by a novel multi-agent deep Q-learning algorithm with joint exploitation sampling. We verify our proposed method on multiple object detection benchmarks. Not only does our model help to improve the performance of state-of-the-art active localization models, it also reveals interesting co-detection patterns that are intuitively interpretable.

Connected through hyperedge HE-1 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-2 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing"):
  with members:
    - "weakly supervised video summarization by hierarchical reinforcement learning"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "ffnet video fast forwarding via reinforcement learning"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "automatic data augmentation by learning the deterministic policy"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "low shot learning from imaginary data"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "feudal networks for hierarchical reinforcement learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "practical block wise neural network architecture generation"

Connected through hyperedge HE-4 (induced by source paper "cooperative reasoning on knowledge graph and corpus a multi agentreinforcement learning approach"):
  with members:
    - "two body problem collaborative visual task completion"
    - "learning when not to answer a ternary reward structure for reinforcement learning based question answering"
    - "deep communicating agents for abstractive summarization"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-5 (induced by source paper "quality aware multimodal saliency detection via deep reinforcement learning"):
  with members:
    - "salient object detection a survey"
    - "attention aware face hallucination via deep reinforcement learning"
    - "a unified rgb t saliency detection benchmark dataset baselines analysis and a novel approach"
    - "perceptual generative adversarial networks for small object detection"
    - "improved training of wasserstein gans"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "tac gan text conditioned auxiliary classifier generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "outline objects using deep reinforcement learning"):
  with members:
    - "path aggregation network for instance segmentation"
    - "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deformable convolutional networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "revisiting the master slave architecture in multi agent deep reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep reinforcement learning an overview"
    - "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-8 (induced by source paper "accnet actor coordinator critic net for learning to communicate with deep multi agent reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "fully decentralized policies for multi agent systems an information theoretic approach"

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)