human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep ordinal classification with inequality constraints"):
  Abstract: This study investigates a new constrained-optimization formulation for deep ordinal classification. We impose uni-modality of the label distribution implicitly via a set of inequality constraints over pairs of adjacent labels. To tackle the ensuing challenging optimization problem, we solve a sequence of unconstrained losses based on a powerful extension of the log-barrier method. This accommodates standard SGD for deep networks, and avoids computationally expensive Lagrangian dual steps and projections, while outperforming substantially penalty methods. Our non-parametric model is more flexible than the existing deep ordinal classification techniques: it does not restrict the learned representation to a specific parametric model, allowing the training to explore larger spaces of solutions and removing the need for ad hoc choices, while scaling up to large numbers of labels. It can be used in conjunction with any standard classification loss and any deep architecture. We also propose a new performance metric for ordinal classification, as a proxy to measure a distribution uni-modality, referred to as the Sides Order Index (SOI). We report comprehensive evaluations and comparisons to state-of-the-art methods on benchmark public datasets for several ordinal classification tasks, showing the merits of our approach in terms of label consistency and scalability. A public reproducible PyTorch implementation is provided (this https URL).

Includes members:
  Member "constrained deep networks lagrangian optimization via log barrier extensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "ordinal distribution regression for gait based age estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "min max entropy for weakly supervised pointwise localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "imposing hard constraints on deep networks promises and limitations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "deep label distribution learning with label ambiguity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "constrained deep weak supervision for histopathology image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non parametric uni modality constraints for deep ordinal classification")
  Member "learning to play in a day faster deep reinforcement learning by optimality tightening":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an inductive bias for distances neural nets that respect the triangle inequality")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)