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: "predict and constrain modeling cardinality in deep structured prediction"
  Abstract: Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently, several deep learning approaches to structured prediction have been proposed. Here we focus on capturing cardinality constraints in such models. Namely, constraining the number of non-zero labels that the model outputs. Such constraints have proven very useful in previous structured prediction approaches, but it is a challenge to introduce them into a deep learning framework. Here we show how to do this via a novel deep architecture. Our approach outperforms strong baselines, achieving state-of-the-art results on multi-label classification benchmarks.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised clustering by exploiting unique class count"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "wide residual networks"
    - "joint unsupervised learning of deep representations and image clusters"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised deep embedding for clustering analysis"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-2 (induced by source paper "the limited multi label projection layer"):
  with members:
    - "linknet relational embedding for scene graph"
    - "smooth loss functions for deep top k classification"
    - "sparse and constrained attention for neural machine translation"
    - "neural motifs scene graph parsing with global context"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "on support relations and semantic scene graphs"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-3 (induced by source paper "a weakly supervised learning based clustering framework"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "a compact network learning model for distribution regression"
    - "predicting time varying distributions with limited training data"
    - "deep sets"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deepsetnet predicting sets with deep neural networks"
    - "group equivariant convolutional networks"
    - "joint unsupervised learning of deep representations and image clusters"

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.LG(Machine Learning)