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: "an end to end approach to natural language object retrieval via context aware deep reinforcement learning"
  Abstract: We propose an end-to-end approach to the natural language object retrieval task, which localizes an object within an image according to a natural language description, i.e., referring expression. Previous works divide this problem into two independent stages: first, compute region proposals from the image without the exploration of the language description; second, score the object proposals with regard to the referring expression and choose the top-ranked proposals. The object proposals are generated independently from the referring expression, which makes the proposal generation redundant and even irrelevant to the referred object. In this work, we train an agent with deep reinforcement learning, which learns to move and reshape a bounding box to localize the object according to the referring expression. We incorporate both the spatial and temporal context information into the training procedure. By simultaneously exploiting local visual information, the spatial and temporal context and the referring language a priori, the agent selects an appropriate action to take at each time. A special action is defined to indicate when the agent finds the referred object, and terminate the procedure. We evaluate our model on various datasets, and our algorithm significantly outperforms the compared algorithms. Notably, the accuracy improvement of our method over the recent method GroundeR and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.

Connected through hyperedge HE-1 (induced by source paper "semi supervised phrase localization in a bidirectional caption image retrieval framework"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "multimodal machine learning a survey and taxonomy"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "deep metric learning via facility location"
    - "grounding of textual phrases in images by reconstruction"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "top down visual saliency guided by captions"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-2 (induced by source paper "pirc net using proposal indexing relationships and context for phrase grounding"):
  with members:
    - "query guided regression network with context policy for phrase grounding"
    - "generating descriptions with grounded and co referenced people"
    - "deep image retrieval learning global representations for image search"
    - "grounding of textual phrases in images by reconstruction"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "modeling context between objects for referring expression understanding"
    - "modeling context in referring expressions"

Connected through hyperedge HE-3 (induced by source paper "rethinking diversified and discriminative proposal generation for visual grounding"):
  with members:
    - "query guided regression network with context policy for phrase grounding"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "grounding of textual phrases in images by reconstruction"
    - "modeling context in referring expressions"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "faster r cnn towards real time object detection with region proposal 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.CV(Computer Vision and Pattern Recognition)