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: "training deep neural networks via direct loss minimization"
  Abstract: Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the application. In this paper we propose a direct loss minimization approach to train deep neural networks, which provably minimizes the application-specific loss function. This is often non-trivial, since these functions are neither smooth nor decomposable and thus are not amenable to optimization with standard gradient-based methods. We demonstrate the effectiveness of our approach in the context of maximizing average precision for ranking problems. Towards this goal, we develop a novel dynamic programming algorithm that can efficiently compute the weight updates. Our approach proves superior to a variety of baselines in the context of action classification and object detection, especially in the presence of label noise.

Connected through hyperedge HE-1 (induced by source paper "optimizing rank based metrics with blackbox differentiation"):
  with members:
    - "learning with average precision training image retrieval with a listwise loss"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "towards accurate one stage object detection with ap loss"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "differentiation of blackbox combinatorial solvers"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "smart predict and optimize for hard combinatorial optimization problems"
    - "end to end learning and optimization on graphs"
    - "learning a sat solver from single bit supervision"

Connected through hyperedge HE-3 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "mihash online hashing with mutual information"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-4 (induced by source paper "viable fast adaptation via backpropagating learned loss"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "alpha maml adaptive model agnostic meta learning"
    - "meta learning via learned loss"
    - "learning to teach with dynamic loss functions"
    - "fast context adaptation via meta learning"
    - "how to train your maml"
    - "evolved policy gradients"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-5 (induced by source paper "towards accurate one stage object detection with ap loss"):
  with members:
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "receptive field block net for accurate and fast object detection"
    - "single shot refinement neural network for object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "an analysis of scale invariance in object detection snip"

Connected through hyperedge HE-6 (induced by source paper "sodeep a sorting deep net to learn ranking loss surrogates"):
  with members:
    - "mediaeval 2018 predicting media memorability task"
    - "simple recurrent units for highly parallelizable recurrence"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"
    - "vse improving visual semantic embeddings with hard negatives"
    - "deep learning the city quantifying urban perception at a global scale"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "direct policy gradients direct optimization of policies in discrete action spaces"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "direct optimization through arg max for discrete variational auto encoder"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "variational bayesian reinforcement learning with regret bounds"
    - "particle value functions"
    - "guided policy search as approximate mirror descent"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-8 (induced by source paper "ap perf incorporating generic performance metrics in differentiable learning"):
  with members:
    - "online and stochastic gradient methods for non decomposable loss functions"
    - "julia a fresh approach to numerical computing"
    - "on the consistency of ordinal regression methods"

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)