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 "parnet position aware aggregated relation network for image text matching"):
  Abstract: Exploring fine-grained relationship between entities(e.g. objects in image or words in sentence) has great contribution to understand multimedia content precisely. Previous attention mechanism employed in image-text matching either takes multiple self attention steps to gather correspondences or uses image objects (or words) as context to infer image-text similarity. However, they only take advantage of semantic information without considering that objects' relative position also contributes to image understanding. To this end, we introduce a novel position-aware relation module to model both the semantic and spatial relationship simultaneously for image-text matching in this paper. Given an image, our method utilizes the location of different objects to capture spatial relationship innovatively. With the combination of semantic and spatial relationship, it's easier to understand the content of different modalities (images and sentences) and capture fine-grained latent correspondences of image-text pairs. Besides, we employ a two-step aggregated relation module to capture interpretable alignment of image-text pairs. The first step, we call it intra-modal relation mechanism, in which we computes responses between different objects in an image or different words in a sentence separately; The second step, we call it inter-modal relation mechanism, in which the query plays a role of textual context to refine the relationship among object proposals in an image. In this way, our position-aware aggregated relation network (ParNet) not only knows which entities are relevant by attending on different objects (words) adaptively, but also adjust the inter-modal correspondence according to the latent alignments according to query's content. Our approach achieves the state-of-the-art results on MS-COCO dataset.

Includes members:
  Member "universal transformers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "look imagine and match improving textual visual cross modal retrieval with generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "relation networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "proposal learning for semi supervised object detection")
  Member "dual path convolutional image text embedding with instance loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "learning robust visual semantic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal rcnn universal object detector via transferable graph r cnn")
  Member "word2visualvec cross media retrieval by visual feature prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of multi view representation learning")

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.AI(Artificial Intelligence)