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: "imposing hard constraints on deep networks promises and limitations"
  Abstract: Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data. Such constraints are usually imposed as soft constraints by adding new terms to the loss function that is minimized during training. An alternative is to impose them as hard constraints, which has a number of theoretical benefits but has not been explored so far due to the perceived intractability of the problem. #R##N#In this paper, we show that imposing hard constraints can in fact be done in a computationally feasible way and delivers reasonable results. However, the theoretical benefits do not materialize and the resulting technique is no better than existing ones relying on soft constraints. We analyze the reasons for this and hope to spur other researchers into proposing better solutions.

Connected through hyperedge HE-1 (induced by source paper "non parametric uni modality constraints for deep ordinal classification"):
  with members:
    - "constrained deep networks lagrangian optimization via log barrier extensions"
    - "ordinal distribution regression for gait based age estimation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "min max entropy for weakly supervised pointwise localization"
    - "deep weakly supervised learning methods for classification and localization in histology images a survey"
    - "deep label distribution learning with label ambiguity"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"

Connected through hyperedge HE-2 (induced by source paper "on constraint definability in tractable probabilistic models"):
  with members:
    - "on constrained open world probabilistic databases"
    - "learning logistic circuits"
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "equality of opportunity in supervised learning"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks"):
  with members:
    - "ai coding learning to construct error correction codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "on deep learning based channel decoding"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-4 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "art abstraction refinement guided training for provably correct neural networks"):
  with members:
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "mixtrain scalable training of formally robust neural networks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"

Connected through hyperedge HE-6 (induced by source paper "constrained deep networks lagrangian optimization via log barrier extensions"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "beyond gradient descent for regularized segmentation losses"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "constrained domain adaptation for segmentation"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "a novel domain adaptation framework for medical image segmentation"
    - "adversarial domain adaptation for classification of prostate histopathology whole slide images"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "adversarial discriminative domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-8 (induced by source paper "log barrier constrained cnns"):
  with members:
    - "constrained cnn losses for weakly supervised segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"

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)