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 "integrate image representation to text model on sentence level a semi supervised framework"):
  Abstract: Integrating visual features has been proved useful in language representation learning. Nevertheless, in most existing multi-modality models, alignment of visual and textual data is prerequisite. In this paper, we propose a novel semi-supervised visual integration framework for sentence level language representation. The uniqueness include: 1) the integration is conducted via a semi-supervised approach, which can bring image to textual NLU tasks by pre-training a visualization network, 2) visual representations are dynamically integrated in both training and predicting stages. To verify the efficacy of the proposed framework, we conduct the experiments on the SemEval 2018 Task 11 and reach new state-of-the-art on this reading comprehension task. Considering that the visual integration framework only requires image database, and no extra alignment is required for training and prediction, it provides an efficient and feasible method for multi-modality language learning.

Includes members:
  Member "multi task deep neural networks for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "discourse marker augmented network with reinforcement learning for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "i know what you want semantic learning for text comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantics aware bert for language understanding")
  Member "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving question answering with external knowledge")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "learning visually grounded sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "vse improving visual semantic embeddings with hard negatives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)