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: "zero shot recognition with unreliable attributes"
  Abstract: In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it possesses---even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.

Connected through hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "out of distribution detection for generalized zero shot action recognition"
    - "transferable contrastive network for generalized zero shot learning"
    - "feedback network for image super resolution"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "depth aware cnn for rgb d segmentation"
    - "multimodal unsupervised image to image translation"
    - "conditional image to image translation"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"

Connected through hyperedge HE-4 (induced by source paper "f vaegan d2 a feature generating framework for any shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "multi modal cycle consistent generalized zero shot learning"
    - "spectral normalization for generative adversarial networks"
    - "low shot learning from imaginary data"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-5 (induced by source paper "semantically tied paired cycle consistency for zero shot sketch based image retrieval"):
  with members:
    - "deep cross modality adaptation via semantics preserving adversarial learning for sketch based 3d shape retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-6 (induced by source paper "guided weak supervision for action recognition with scarce data to assess skills of children with autism"):
  with members:
    - "out of distribution detection for generalized zero shot action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "temporal segment networks towards good practices for deep action recognition"
    - "objects2action classifying and localizing actions without any video example"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-7 (induced by source paper "zero shot feature selection via exploiting semantic knowledge"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "multi cue zero shot learning with strong supervision"
    - "unsupervised feature selection with adaptive structure learning"

Connected through hyperedge HE-8 (induced by source paper "classifier and exemplar synthesis for zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "the devil is in the tails fine grained classification in the wild"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "a generative adversarial approach for zero shot learning from noisy texts"

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)