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: "variational methods for conditional multimodal learning generating human faces from attributes"
  Abstract: Prior to this decade, the field of computer vision was primarily focused around hand-crafted feature extraction methods used in conjunction with discriminative models for specific tasks such as object recognition, detection/localization, tracking etc. A generative image understanding was neither within reach nor the prime concern of the period. In this paper, we address the following problem: Given a description of a human face, can we generate the image corresponding to it? We frame this problem as a conditional modality learning problem and use variational methods for maximizing the corresponding conditional log-likelihood. The resultant deep model, which we refer to as conditional multimodal autoencoder (CMMA), forces the latent representation obtained from the attributes alone to be 'close' to the joint representation obtained from both face and attributes. We show that the faces generated from attributes using the proposed model, are qualitatively and quantitatively more representative of the attributes from which they were generated, than those obtained by other deep generative models. We also propose a secondary task, whereby the existing faces are modified by modifying the corresponding attributes. We observe that the modifications in face introduced by the proposed model are representative of the corresponding modifications in attributes. Hence, our proposed method solves the above mentioned problem.

Connected through hyperedge HE-1 (induced by source paper "diversifying topic coherent response generation for natural multi turn conversations"):
  with members:
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "multi level memory for task oriented dialogs"
    - "variational memory encoder decoder"
    - "language gans falling short"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "diverse beam search for increased novelty in abstractive summarization"
    - "toward controlled generation of text"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-2 (induced by source paper "a classification supervised auto encoder based on predefined evenly distributed class centroids"):
  with members:
    - "continual classification learning using generative models"
    - "non adversarial image synthesis with generative latent nearest neighbors"
    - "additive margin softmax for face verification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "emnist an extension of mnist to handwritten letters"
    - "squeeze and excitation networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "an uncertain future forecasting from static images using variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "dense supervision for visual comparisons via synthetic images"):
  with members:
    - "convolutional network for attribute driven and identity preserving human face generation"
    - "variational methods for conditional multimodal deep learning"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "attribute2image conditional image generation from visual attributes"
    - "deep relative attributes"
    - "spatial transformer networks"
    - "draw a recurrent neural network for image generation"
    - "synthetic data and artificial neural networks for natural scene text 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)