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 "attributes as operators factorizing unseen attribute object compositions"):
  Abstract: We present a new approach to modeling visual attributes. Prior work casts attributes in a similar role as objects, learning a latent representation where properties (e.g., sliced) are recognized by classifiers much in the way objects (e.g., apple) are. However, this common approach fails to separate the attributes observed during training from the objects with which they are composed, making it ineffectual when encountering new attribute-object compositions. Instead, we propose to model attributes as operators. Our approach learns a semantic embedding that explicitly factors out attributes from their accompanying objects, and also benefits from novel regularizers expressing attribute operators' effects (e.g., blunt should undo the effects of sharp). Not only does our approach align conceptually with the linguistic role of attributes as modifiers, but it also generalizes to recognize unseen compositions of objects and attributes. We validate our approach on two challenging datasets and demonstrate significant improvements over the state-of-the-art. In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.

Includes members:
  Member "neural algebra of classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aesthetic image captioning from weakly labelled photographs")
  Member "zero shot visual recognition using semantics preserving adversarial embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "joint discovery of object states and manipulation actions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "alternative semantic representations for zero shot human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crl class representative learning for image classification")
  Member "learning the latent look unsupervised discovery of a style coherent embedding from fashion images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty")
  Member "a generative adversarial approach for zero shot learning from noisy texts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")
  Member "feature generating networks for zero shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification")
  Member "zero shot learning the good the bad and the ugly":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crl class representative learning for image classification")

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)