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: "generating captions without looking beyond objects"
  Abstract: This paper explores new evaluation perspectives for image captioning and introduces a noun translation task that achieves comparative image caption generation performance by translating from a set of nouns to captions. This implies that in image captioning, all word categories other than nouns can be evoked by a powerful language model without sacrificing performance on the precision-oriented metric BLEU. The paper also investigates lower and upper bounds of how much individual word categories in the captions contribute to the final BLEU score. A large possible improvement exists for nouns, verbs, and prepositions.

Connected through hyperedge HE-1 (induced by source paper "middle out decoding"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural program search solving programming tasks from description and examples"
    - "texygen a benchmarking platform for text generation models"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning structured text representations"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-2 (induced by source paper "on the effectiveness of task granularity for transfer learning"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "the something something video database for learning and evaluating visual common sense"
    - "temporal tessellation a unified approach for video analysis"
    - "oracle performance for visual captioning"

Connected through hyperedge HE-3 (induced by source paper "the something something video database for learning and evaluating visual common sense"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "dense captioning events in videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning physical intuition of block towers by example"
    - "the curious robot learning visual representations via physical interactions"
    - "oracle performance for visual captioning"

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)