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: "heated up softmax embedding"
  Abstract: Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples. The problem is generally solved by learning an embedding for each sample such that the embeddings of samples of the same category are compact while the embeddings of samples of different categories are spread-out in the feature space. We study the features extracted from the second last layer of a deep neural network based classifier trained with the cross entropy loss on top of the softmax layer. We show that training classifiers with different temperature values of softmax function leads to features with different levels of compactness. Leveraging these insights, we propose a "heating-up" strategy to train a classifier with increasing temperatures, leading the corresponding embeddings to achieve state-of-the-art performance on a variety of metric learning benchmarks.

Connected through hyperedge HE-1 (induced by source paper "circle loss a unified perspective of pair similarity optimization"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "softmax dissection towards understanding intra and inter class objective for embedding learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-3 (induced by source paper "combination of multiple global descriptors for image retrieval"):
  with members:
    - "large scale landmark retrieval recognition under a noisy and diverse dataset"
    - "2nd place and 2nd place solution to kaggle landmark recognition andretrieval competition 2019"
    - "visualizing deep similarity networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "learning discriminative features with multiple granularities for person re identification"
    - "classification is a strong baseline for deep metric learning"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning beyond binary supervision"
    - "decoupled networks"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-5 (induced by source paper "derivative manipulation for adjusting emphasis density function a general example weighting framework"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "improving mae against cce under label noise"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "deep metric learning by online soft mining and class aware attention"

Connected through hyperedge HE-6 (induced by source paper "variational metric scaling for metric based meta learning"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "on first order meta learning algorithms"
    - "rethinking feature distribution for loss functions in image classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "few shot object detection via feature reweighting"
    - "gaussian prototypical networks for few shot learning on omniglot"
    - "probabilistic model agnostic meta learning"
    - "large margin few shot learning"

Connected through hyperedge HE-7 (induced by source paper "needle in a haystack a framework for seeking small objects in big datasets"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "spotting the difference context retrieval and analysis for improved forgery detection and localization"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "region based image retrieval revisited"
    - "learning spread out local feature descriptors"

Connected through hyperedge HE-8 (induced by source paper "improved embeddings with easy positive triplet mining"):
  with members:
    - "hotels 50k a global hotel recognition dataset"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "no fuss distance metric learning using proxies"
    - "hard aware deeply cascaded embedding"
    - "deep feature learning with relative distance comparison for person re identification"

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)