human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a cnn rnn framework for image annotation from visual cues and social network metadata"):
  Abstract: Images represent a commonly used form of visual communication among people. Nevertheless, image classification may be a challenging task when dealing with unclear or non-common images needing more context to be correctly annotated. Metadata accompanying images on social-media represent an ideal source of additional information for retrieving proper neighbourhoods easing image annotation task. To this end, we blend visual features extracted from neighbours and their metadata to jointly leverage context and visual cues. Our models use multiple semantic embeddings to properly map metadata to a meaningful semantic space decoupling the neural model from the low-level representation of metadata and achieve robustness to vocabulary changes between training and testing phases. Convolutional and recurrent neural networks (CNNs-RNNs) are jointly adopted to infer similarity among neighbours and query images. We perform comprehensive experiments on the NUS-WIDE dataset showing that our models outperform state-of-the-art architectures based on images and metadata, and decrease both sensory and semantic gaps to better annotate images.

Includes members:
  Member "modeling visual context is key to augmenting object detection datasets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "deep neural networks in fully connected crf for image labeling with social network metadata":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "iterative and adaptive sampling with spatial attention for black box model explanations")
  Member "learning spatial regularization with image level supervisions for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")
  Member "automatic image annotation via label transfer in the semantic space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi label image classification via knowledge distillation from weakly supervised detection")
  Member "semantic regularisation for recurrent image annotation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved hard example mining by discovering attribute based hard person identity")
  Member "cnn rnn a unified framework for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learn to predict sets using feed forward neural networks")
  Member "webly supervised learning of convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "learning structured inference neural networks with label relations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "orderless recurrent models for multi label classification")

Question: Please tell me which class the source paper 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)