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: "co training for demographic classification using deep learning from label proportions"
  Abstract: Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we introduce a deep neural network for the Learning from Label Proportion (LLP) setting, in which the training data consist of bags of unlabeled instances with associated label distributions for each bag. We introduce a new regularization layer, Batch Averager, that can be appended to the last layer of any deep neural network to convert it from supervised learning to LLP. This layer can be implemented readily with existing deep learning packages. To further support domains in which the data consist of two conditionally independent feature views (e.g. image and text), we propose a co-training algorithm that iteratively generates pseudo bags and refits the deep LLP model to improve classification accuracy. We demonstrate our models on demographic attribute classification (gender and race/ethnicity), which has many applications in social media analysis, public health, and marketing. We conduct experiments to predict demographics of Twitter users based on their tweets and profile image, without requiring any user-level annotations for training. We find that the deep LLP approach outperforms baselines for both text and image features separately. Additionally, we find that co-training algorithm improves image and text classification by 4% and 8% absolute F1, respectively. Finally, an ensemble of text and image classifiers further improves the absolute F1 measure by 4% on average.

Connected through hyperedge HE-1 (induced by source paper "demographic inference and representative population estimates from multilingual social media data"):
  with members:
    - "on the convergence of adam and beyond"
    - "bleaching text abstract features for cross lingual gender prediction"
    - "word translation without parallel data"
    - "densely connected convolutional networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "whom should we sense in social sensing analyzing which users work best for social media now casting"
    - "character aware neural language models"
    - "morphological inflection generation using character sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "learning from label proportions with consistency regularization"):
  with members:
    - "deep multi class learning from label proportions"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning from label proportions for emphysema quantification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "temporal ensembling for semi supervised learning"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning from label proportions with generative adversarial networks"):
  with members:
    - "deep multi class learning from label proportions"
    - "relational inductive biases deep learning and graph networks"
    - "good semi supervised learning that requires a bad gan"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "you only look once unified real time object detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep multi class learning from label proportions"):
  with members:
    - "deep learning from label proportions for emphysema quantification"
    - "deep residual learning for image recognition"
    - "learning with a wasserstein loss"
    - "learning about individuals from group statistics"

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)