human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  Abstract: Pre-trained language models such as BERT have recently contributed to significant advances in Natural Language Processing tasks. Interestingly, while multilingual BERT models have demonstrated impressive results, recent works have shown how monolingual BERT can also be competitive in zero-shot cross-lingual settings. This suggests that the abstractions learned by these models can transfer across languages, even when trained on monolingual data. In this paper, we investigate whether such generalization potential applies to other modalities, such as vision: does BERT contain abstractions that generalize beyond text? We introduce BERT-gen, an architecture for text generation based on BERT, able to leverage on either mono- or multi- modal representations. The results reported under different configurations indicate a positive answer to our research question, and the proposed model obtains substantial improvements over the state-of-the-art on two established Visual Question Generation datasets.

Includes members:
  Member "deep bayesian network for visual question generation":
    also connected through other hyperedges:
      (none)
  Member "incorporating visual semantics into sentence representations within a grounded space":
    also connected through other hyperedges:
      (none)
  Member "visualizing and measuring the geometry of bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "lxmert learning cross modality encoder representations from transformers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "in defense of grid features for visual question answering")
  Member "bert rediscovers the classical nlp pipeline":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "context aware zero shot learning for object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot learning with contextual cueing for object recognition in complex scenes")
  Member "fusion of detected objects in text for visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "xgpt cross modal generative pre training for image captioning")
  Member "supervised multimodal bitransformers for classifying images and text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep multi modal sets")

Question: Please tell me which class the source paper 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.CL(Computation and Language)