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 "learning similarity conditions without explicit supervision"):
  Abstract: Many real-world tasks require models to compare images along multiple similarity conditions (e.g. similarity in color, category or shape). Existing methods often reason about these complex similarity relationships by learning condition-aware embeddings. While such embeddings aid models in learning different notions of similarity, they also limit their capability to generalize to unseen categories since they require explicit labels at test time. To address this deficiency, we propose an approach that jointly learns representations for the different similarity conditions and their contributions as a latent variable without explicit supervision. Comprehensive experiments across three datasets, Polyvore-Outfits, Maryland-Polyvore and UT-Zappos50k, demonstrate the effectiveness of our approach: our model outperforms the state-of-the-art methods, even those that are strongly supervised with pre-defined similarity conditions, on fill-in-the-blank, outfit compatibility prediction and triplet prediction tasks. Finally, we show that our model learns different visually-relevant semantic sub-spaces that allow it to generalize well to unseen categories.

Includes members:
  Member "context aware visual compatibility prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "learning type aware embeddings for fashion compatibility":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "give me a hint navigating image databases using human in the loop feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised feature level attribute manipulation for fashion image retrieval")
  Member "learning two branch neural networks for image text matching tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "conditional image text embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "creating capsule wardrobes from fashion images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "learning the latent look unsupervised discovery of a style coherent embedding from fashion images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")

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)