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: "the google similarity distance"
  Abstract: Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wide-web using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies, and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87% with the expert crafted WordNet categories.

Connected through hyperedge HE-1 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "knowledge based word sense disambiguation using topic models"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "query expansion techniques for information retrieval a survey"):
  with members:
    - "just an update on pming distance for web based semantic similarity in artificial intelligence and data mining"
    - "using word embeddings for automatic query expansion"
    - "query expansion with locally trained word embeddings"
    - "query expansion via structural motifs in wikipedia graph"
    - "query expansion based on crowd knowledge for code search"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "exploring query categorisation for query expansion a study"
    - "query expansion using term distribution and term association"

Connected through hyperedge HE-3 (induced by source paper "predicting conceptnet path quality using crowdsourced assessments of naturalness"):
  with members:
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "modeling relation paths for representation learning of knowledge bases"
    - "traversing knowledge graphs in vector space"
    - "compositional vector space models for knowledge base completion"
    - "adam a method for stochastic optimization"
    - "retrofitting word vectors to semantic lexicons"

Connected through hyperedge HE-4 (induced by source paper "few shot learning based human activity recognition"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "deep recurrent neural network for mobile human activity recognition with high throughput"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "extracting visual knowledge from the internet making sense of image data"):
  with members:
    - "towards automatic construction of diverse high quality image dataset"
    - "exploiting web images for dataset construction a domain robust approach"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-6 (induced by source paper "query oriented text summarization based on hypergraph transversals"):
  with members:
    - "the minimal hitting set generation problem algorithms and computation"
    - "different approaches for identifying important concepts in probabilistic biomedical text summarization"
    - "learning topic models going beyond svd"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-7 (induced by source paper "combining neural and knowledge based approaches to named entity recognition in polish"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "design challenges and misconceptions in neural sequence labeling"
    - "unsupervised learning of sentence representations using sequence consistency"
    - "deep contextualized word representations"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-8 (induced by source paper "neural article pair modeling for wikipedia sub article matching"):
  with members:
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "on2vec embedding based relation prediction for ontology population"
    - "reading wikipedia to answer open domain questions"
    - "text understanding with the attention sum reader network"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "gated attention readers for text comprehension"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

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.CL(Computation and Language)