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: "recurrent models for situation recognition"
  Abstract: This work proposes Recurrent Neural Network (RNN) models to predict structured image situations - actions and noun entities fulfilling semantic roles related to the action. We transform the task of noun entity prediction to that of sequential prediction and use RNNs in contrast to prior work that uses Conditional Random Fields (CRFs). By using an action prediction network and a separate network with an RNN for noun prediction, we obtain state-of-the-art accuracy on the challenging recent imSitu dataset, beating CRF-based models, including ones trained with additional data. Further, we show that specialized features learned from situation prediction can be transferred to the task of image captioning to more accurately describe human-object interactions.

Connected through hyperedge HE-1 (induced by source paper "multimodal grounding for language processing"):
  with members:
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "interactive natural language acquisition in a multi modal recurrent neural architecture"
    - "multimodal named entity recognition for short social media posts"
    - "modulating early visual processing by language"
    - "multimodal machine learning a survey and taxonomy"
    - "gated attention architectures for task oriented language grounding"
    - "emergence of grounded compositional language in multi agent populations"
    - "teaching machines to describe images via natural language feedback"

Connected through hyperedge HE-2 (induced by source paper "convolutional image captioning"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "maskrnn instance level video object segmentation"
    - "attention is all you need"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "creativity generating diverse questions using variational autoencoders"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "convolutional sequence to sequence learning"

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)