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: "smooth loss functions for deep top k classification"
  Abstract: The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in the limit of infinite data. In the context of limited and noisy data however, the use of a loss function that is specifically designed for top-k classification can bring significant improvements. Our empirical evidence suggests that the loss function must be smooth and have non-sparse gradients in order to work well with deep neural networks. Consequently, we introduce a family of smoothed loss functions that are suited to top-k optimization via deep learning. The widely used cross-entropy is a special case of our family. Evaluating our smooth loss functions is computationally challenging: a na\"ive algorithm would require $\mathcal{O}(\binom{n}{k})$ operations, where n is the number of classes. Thanks to a connection to polynomial algebra and a divide-and-conquer approach, we provide an algorithm with a time complexity of $\mathcal{O}(k n)$. Furthermore, we present a novel approximation to obtain fast and stable algorithms on GPUs with single floating point precision. We compare the performance of the cross-entropy loss and our margin-based losses in various regimes of noise and data size, for the predominant use case of k=5. Our investigation reveals that our loss is more robust to noise and overfitting than cross-entropy.

Connected through hyperedge HE-1 (induced by source paper "anchor loss modulating loss scale based on prediction difficulty"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "focal loss for dense object detection"
    - "a general and adaptive robust loss function"

Connected through hyperedge HE-2 (induced by source paper "imae for noise robust learning mean absolute error does not treat examples equally and gradient magnitude s variance matters"):
  with members:
    - "deep self learning from noisy labels"
    - "ranked list loss for deep metric learning"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "id aware quality for set based person re identification"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "deep metric learning by online soft mining and class aware attention"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-3 (induced by source paper "computational ceramicology"):
  with members:
    - "photorealistic image synthesis for object instance detection"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to drive from simulation without real world labels"
    - "focal loss for dense object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-4 (induced by source paper "the limited multi label projection layer"):
  with members:
    - "linknet relational embedding for scene graph"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "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-5 (induced by source paper "autocorrect deep inductive alignment of noisy geometric annotations"):
  with members:
    - "inductive visual localisation factorised training for superior generalisation"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "cycada cycle consistent adversarial domain adaptation"
    - "making neural programming architectures generalize via recursion"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "spatial memory for context reasoning in object detection"
    - "training a feedback loop for hand pose estimation"

Connected through hyperedge HE-6 (induced by source paper "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "data efficient image recognition with contrastive predictive coding"
    - "cluster based learning from weakly labeled bags in digital pathology"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "attention based deep multiple instance learning"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "classification of breast cancer histology using deep learning"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-7 (induced by source paper "predicting human activities from user generated content"):
  with members:
    - "event2mind commonsense inference on events intents and reactions"
    - "direct network transfer transfer learning of sentence embeddings for semantic similarity"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "augur mining human behaviors from fiction to power interactive systems"
    - "sort story sorting jumbled images and captions into stories"
    - "advances in human action recognition a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "on the consistency of top k surrogate losses"):
  with members:
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "very deep convolutional networks for large scale image recognition"

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)