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: "discovering visual concept structure with sparse and incomplete tags"
  Abstract: Discovering automatically the semantic structure of tagged visual data (e.g. web videos and images) is important for visual data analysis and interpretation, enabling the machine intelligence for effectively processing the fast-growing amount of multi-media data. However, this is non-trivial due to the need for jointly learning underlying correlations between heterogeneous visual and tag data. The task is made more challenging by inherently sparse and incomplete tags. In this work, we develop a method for modelling the inherent visual data concept structures based on a novel Hierarchical-Multi-Label Random Forest model capable of correlating structured visual and tag information so as to more accurately interpret the visual semantics, e.g. disclosing meaningful visual groups with similar high-level concepts, and recovering missing tags for individual visual data samples. Specifically, our model exploits hierarchically structured tags of different semantic abstractness and multiple tag statistical correlations in addition to modelling visual and tag interactions. As a result, our model is able to discover more accurate semantic correlation between textual tags and visual features, and finally providing favourable visual semantics interpretation even with highly sparse and incomplete tags. We demonstrate the advantages of our proposed approach in two fundamental applications, visual data clustering and missing tag completion, on benchmarking video (i.e. TRECVID MED 2011) and image (i.e. NUS-WIDE) datasets.

Connected through hyperedge HE-1 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "learning deep sketch abstraction"
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "neural extractive summarization with side information"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-2 (induced by source paper "video summarisation by classification with deep reinforcement learning"):
  with members:
    - "dilated temporal relational adversarial network for generic video summarization"
    - "learning deep sketch abstraction"
    - "ffnet video fast forwarding via reinforcement learning"
    - "learning video summarization using unpaired data"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "actor critic sequence training for image captioning"
    - "video captioning via hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"

Connected through hyperedge HE-3 (induced by source paper "attribute recognition by joint recurrent learning of context and correlation"):
  with members:
    - "class rectification hard mining for imbalanced deep learning"
    - "transferring a semantic representation for person re identification and search"
    - "deep attributes driven multi camera person re identification"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "semantic regularisation for recurrent image annotation"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "cnn rnn a unified framework for multi label image classification"
    - "sequential person recognition in photo albums with a recurrent network"

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)