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 "pose guided joint global and attentive local matching network for text based person search"):
  Abstract: Text-based person search aims to retrieve the corresponding persons in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different levels of semantic relevance. To exploit the multilevel relevances between human description and corresponding visual contents, we propose a pose-guided joint global and attentive local matching network (GALM), which includes global, uni-local and bi-local matching. The global matching network aims to learn global cross-modal representations. To further capture the meaningful local relations, we propose an uni-local matching network to compute the local similarities between image regions and textual description and then utilize a similarity-based hard attention to select the description-related image regions. In addition to sentence-level matching, the fine-grained phrase-level matching is captured by the bi-local matching network, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 \% in terms of top-1 metric.

Includes members:
  Member "learning visual question answering by bootstrapping hard attention":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "identity aware textual visual matching with latent co attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "ai challenger a large scale dataset for going deeper in image understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep high resolution representation learning for human pose estimation")
  Member "person search with natural language description":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross modality person re identification with shared specific feature transfer")
  Member "dual path convolutional image text embedding with instance loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diversity achieving slow dropblock network for person re identification")
  Member "vse improving visual semantic embeddings with hard negatives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "pose invariant embedding for deep person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")

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.CV(Computer Vision and Pattern Recognition)