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: "representation learning for very short texts using weighted word embedding aggregation"
  Abstract: We create text representations by weighing word embeddings using idf information.A novel median-based loss is designed to mitigate the negative effect of outliers.A dataset of semantically related textual pairs from Wikipedia and Twitter is made.Our method outperforms all word embedding baselines in a semantic similarity task.Our method is out-of-the-box and thus requires no retraining in different contexts. Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc. We constructed a method based on semantic word embeddings and frequency information to arrive at low-dimensional representations for short texts designed to capture semantic similarity. For this purpose we designed a weight-based model and a learning procedure based on a novel median-based loss function. This paper discusses the details of our model and the optimization methods, together with the experimental results on both Wikipedia and Twitter data. We find that our method outperforms the baseline approaches in the experiments, and that it generalizes well on different word embeddings without retraining. Our method is therefore capable of retaining most of the semantic information in the text, and is applicable out-of-the-box.

Connected through hyperedge HE-1 (induced by source paper "interpretable segmentation of medical free text records based on word embeddings"):
  with members:
    - "insights into analogy completion from the biomedical domain"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "efficient estimation of word representations in vector space"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "correlations between word vector sets"):
  with members:
    - "correlation coefficients and semantic textual similarity"
    - "similarity of neural network representations revisited"
    - "model comparison for semantic grouping"
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"

Connected through hyperedge HE-4 (induced by source paper "correlation coefficients and semantic textual similarity"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "don t settle for average go for the max fuzzy sets and max pooled word vectors"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-6 (induced by source paper "large scale collaborative filtering with product embeddings"):
  with members:
    - "deep sets"
    - "structured attention networks"
    - "starspace embed all the things"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "deep cross network for ad click predictions"
    - "specializing joint representations for the task of product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "tell me what to track"):
  with members:
    - "lasot a high quality benchmark for large scale single object tracking"
    - "vital visual tracking via adversarial learning"
    - "person search with natural language description"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "where to play retrieval of video segments using natural language queries"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "detect to track and track to detect"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "learning structured text representations"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "neural discourse structure for text categorization"
    - "better document level sentiment analysis from rst discourse parsing"
    - "improved semantic representations from tree structured long short term memory networks"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

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.IR(Information Retrieval)