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: "a unified semantic embedding relating taxonomies and attributes"
  Abstract: We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. Contrary to prior work which only utilized them as side information, we explicitly embed the semantic entities into the same space where we embed categories, which enables us to represent a category as their linear combination. By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn discriminative composition.

Connected through hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "not just a matter of semantics the relationship between visual similarity and semantic similarity"
    - "do convolutional neural networks learn class hierarchy"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "the inaturalist species classification and detection dataset"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "link the head to the beak zero shot learning from noisy text description at part precision"):
  with members:
    - "write a classifier predicting visual classifiers from unstructured text descriptions"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "multi cue zero shot learning with strong supervision"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "learning hypergraph regularized attribute predictors"

Connected through hyperedge HE-3 (induced by source paper "zero shot recognition using dual visual semantic mapping paths"):
  with members:
    - "learning deep representations of fine grained visual descriptions"
    - "synthesized classifiers for zero shot learning"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"
    - "transductive multi view zero shot learning"
    - "learning hypergraph regularized attribute predictors"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "learning attributes equals multi source domain generalization"):
  with members:
    - "weakly supervised learning of objects attributes and their associations"
    - "learning hypergraph regularized attribute predictors"
    - "automatic concept discovery from parallel text and visual corpora"
    - "evaluation of output embeddings for fine grained image classification"
    - "domain generalization for object recognition with multi task autoencoders"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "semi supervised vocabulary informed learning"):
  with members:
    - "transductive multi view zero shot learning"
    - "towards open world recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "prediction of search targets from fixations in open world settings"
    - "zero shot recognition with unreliable attributes"
    - "return of the devil in the details delving deep into convolutional nets"
    - "learning distributed word representations for natural logic reasoning"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-6 (induced by source paper "zero shot learning via semantic similarity embedding"):
  with members:
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "probabilistic zero shot classification with semantic rankings"
    - "group membership prediction"
    - "evaluation of output embeddings for fine grained image classification"
    - "prism person re identification via structured matching"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-7 (induced by source paper "learning structured inference neural networks with label relations"):
  with members:
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "fully connected deep structured networks"
    - "probabilistic label relation graphs with ising models"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "learning hypergraph regularized attribute predictors"):
  with members:
    - "zero shot recognition with unreliable attributes"
    - "caffe convolutional architecture for fast feature embedding"
    - "on the effect of hyperedge weights on hypergraph learning"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)