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: "learning to play in a day faster deep reinforcement learning by optimality tightening"
  Abstract: We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.

Connected through hyperedge HE-1 (induced by source paper "an inductive bias for distances neural nets that respect the triangle inequality"):
  with members:
    - "a projection method for metric constrained optimization"
    - "sorting out lipschitz function approximation"
    - "on evaluation of embodied navigation agents"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "prototypical networks for few shot learning"
    - "node2vec scalable feature learning for networks"
    - "input convex neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (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"
    - "imposing hard constraints on deep networks promises and limitations"
    - "deep label distribution learning with label ambiguity"
    - "constrained deep weak supervision for histopathology image segmentation"

Connected through hyperedge HE-3 (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"
    - "imposing hard constraints on deep networks promises and limitations"

Connected through hyperedge HE-4 (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"
    - "imposing hard constraints on deep networks promises and limitations"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (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"
    - "imposing hard constraints on deep networks promises and limitations"
    - "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"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "dual indicators to analyse ai benchmarks difficulty discrimination ability and generality"):
  with members:
    - "accounting for the neglected dimensions of ai progress"
    - "re evaluating evaluation"
    - "a multidisciplinary task based perspective for evaluating the impact of ai autonomy and generality on the future of work"
    - "neural episodic control"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "pgq combining policy gradient and q learning"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "a data efficient deep learning approach for deployable multimodal social robots"):
  with members:
    - "deep reinforcement learning for chatbots using clustered actions and human likeness rewards"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "asynchronous methods for deep reinforcement learning"
    - "training an interactive humanoid robot using multimodal deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"

Connected through hyperedge HE-8 (induced by source paper "deep ordinal classification with inequality constraints"):
  with members:
    - "constrained deep networks lagrangian optimization via log barrier extensions"
    - "ordinal distribution regression for gait based age estimation"
    - "min max entropy for weakly supervised pointwise localization"
    - "imposing hard constraints on deep networks promises and limitations"
    - "deep label distribution learning with label ambiguity"
    - "constrained deep weak supervision for histopathology image segmentation"
    - "deep residual learning for image recognition"
    - "constrained convolutional neural networks for weakly supervised 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.LG(Machine Learning)