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: "object sensitive deep reinforcement learning"
  Abstract: Deep reinforcement learning has become popular over recent years, showing superiority on different visual-input tasks such as playing Atari games and robot navigation. Although objects are important image elements, few work considers enhancing deep reinforcement learning with object characteristics. In this paper, we propose a novel method that can incorporate object recognition processing to deep reinforcement learning models. This approach can be adapted to any existing deep reinforcement learning frameworks. State-of-the-art results are shown in experiments on Atari games. We also propose a new approach called "object saliency maps" to visually explain the actions made by deep reinforcement learning agents.

Connected through hyperedge HE-1 (induced by source paper "self supervised object level deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "entity abstraction in visual model based reinforcement learning"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "unsupervised video object segmentation for deep reinforcement learning"
    - "synthesizing neural network controllers with probabilistic model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "transition based dependency parsing using perceptron learner"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "modeling product search relevance in e commerce"
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "transparency and explanation in deep reinforcement learning neural networks"

Connected through hyperedge HE-3 (induced by source paper "modeling product search relevance in e commerce"):
  with members:
    - "a correspondence analysis framework for author conference recommendations"
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"

Connected through hyperedge HE-4 (induced by source paper "a correspondence analysis framework for author conference recommendations"):
  with members:
    - "simultaneous identification of tweet purpose and position"
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a machine learning framework for authorship identification from texts"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"

Connected through hyperedge HE-5 (induced by source paper "simultaneous identification of tweet purpose and position"):
  with members:
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"

Connected through hyperedge HE-6 (induced by source paper "an unsupervised domain independent framework for automated detection of persuasion tactics in text"):
  with members:
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "content based video indexing and retrieval using corr lda"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "a heterogeneous graphical model to understand user level sentiments in social media"):
  with members:
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"
    - "user level sentiment analysis incorporating social networks"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "a machine learning framework for authorship identification from texts"):
  with members:
    - "an unsupervised domain independent framework for automated detection of persuasion tactics in text"
    - "a heterogeneous graphical model to understand user level sentiments in social media"
    - "event outcome prediction using sentiment analysis and crowd wisdom in microblog feeds"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "photorealistic style transfer for videos"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "content based video indexing and retrieval using corr lda"

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.LG(Machine Learning)