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: "learning hypergraph regularized attribute predictors"
  Abstract: We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and $N$-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries"):
  with members:
    - "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"
    - "heterogeneous network motifs"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "hypergraph clustering with categorical edge labels"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "rept a streaming algorithm of approximating global and local triangle counts in parallel"
    - "simplicial closure and higher order link prediction"

Connected through hyperedge HE-2 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "hypergraph neural networks"
    - "simplicial closure and higher order link prediction"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "node2vec scalable feature learning for networks"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "predicting multi actor collaborations using hypergraphs"

Connected through hyperedge HE-3 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-4 (induced by source paper "transfer learning via unsupervised task discovery for visual question answering"):
  with members:
    - "neural baby talk"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "dvqa understanding data visualizations via question answering"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "extreme clicking for efficient object annotation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"

Connected through hyperedge HE-5 (induced by source paper "attributes as operators factorizing unseen attribute object compositions"):
  with members:
    - "neural algebra of classifiers"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint discovery of object states and manipulation actions"
    - "alternative semantic representations for zero shot human action recognition"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-6 (induced by source paper "few shot adaptation for multimedia semantic indexing"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "semantic autoencoder for zero shot learning"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "zero shot learning the good the bad and the ugly"
    - "learning a deep embedding model for zero shot learning"
    - "places an image database for deep scene understanding"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-7 (induced by source paper "attributes as operators"):
  with members:
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint discovery of object states and manipulation actions"
    - "alternative semantic representations for zero shot human action recognition"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "visual translation embedding network for visual relation detection"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"

Connected through hyperedge HE-8 (induced by source paper "an empirical evaluation of visual question answering for novel objects"):
  with members:
    - "learning to compose neural networks for question answering"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "visual question answering a survey of methods and datasets"
    - "fvqa fact based visual question answering"
    - "latent embeddings for zero shot classification"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "ask me anything free form visual question answering based on knowledge from external sources"

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)