human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "attributes as operators"
  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.

Connected through hyperedge HE-1 (induced by source paper "tafe net task aware feature embeddings for low shot learning"):
  with members:
    - "low shot learning from imaginary data"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "modulating early visual processing by language"
    - "cycada cycle consistent adversarial domain adaptation"
    - "idk cascades fast deep learning by learning not to overthink"
    - "prototypical networks for few shot learning"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-2 (induced by source paper "tafe net task aware feature embeddings for efficient learning and inference"):
  with members:
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "low shot learning from imaginary data"
    - "skipnet learning dynamic routing in convolutional networks"
    - "modulating early visual processing by language"
    - "data driven sparse structure selection for deep neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "idk cascades fast deep learning by learning not to overthink"
    - "deep layer aggregation"

Connected through hyperedge HE-3 (induced by source paper "composing text and image for image retrieval an empirical odyssey"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "dynamic few shot visual learning without forgetting"
    - "dialog based interactive image retrieval"
    - "in defense of the triplet loss for person re identification"
    - "language based image editing with recurrent attentive models"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "ivqa inverse visual question answering"

Question: Please tell me which class the center node 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)