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: "fine grained image classification via combining vision and language"
  Abstract: Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combing vision and language (CVL) for learning latent semantic representations. The vision stream learns deep representations from the original visual information via deep convolutional neural network. The language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combing the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"

Connected through hyperedge HE-3 (induced by source paper "deep learning for fine grained image analysis a survey"):
  with members:
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "rpc a large scale retail product checkout dataset"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "multi attention multi class constraint for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "coarse to fine a rnn based hierarchical attention model for vehicle re identification"
    - "part aligned bilinear representations for person re identification"

Connected through hyperedge HE-4 (induced by source paper "shaping visual representations with language for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "using natural language for reward shaping in reinforcement learning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "grounding visual explanations"
    - "e snli natural language inference with natural language explanations"
    - "learning to understand goal specifications by modelling reward"

Connected through hyperedge HE-5 (induced by source paper "hierarchical annotation of images with two alternative forced choice metric learning"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "deep metric learning with hierarchical triplet loss"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "low rank bilinear pooling for fine grained classification"
    - "disc deep image saliency computing via progressive representation learning"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "compact bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "a new benchmark and approach for fine grained cross media retrieval"):
  with members:
    - "fine grained video categorization with redundancy reduction attention"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "mhtn modal adversarial hybrid transfer network for cross modal retrieval"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-7 (induced by source paper "large scale fine grained categorization and domain specific transfer learning"):
  with members:
    - "teaching categories to human learners with visual explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "generalized orderless pooling performs implicit salient matching"
    - "the devil is in the tails fine grained classification in the wild"
    - "learning transferable architectures for scalable image recognition"
    - "deep layer aggregation"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "squeeze and excitation networks"

Connected through hyperedge HE-8 (induced by source paper "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"):
  with members:
    - "image generation from scene graphs"
    - "semi supervised fusedgan for conditional image generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unsupervised learning of disentangled representations from video"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"

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)