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: "setexpan corpus based set expansion via context feature selection and rank ensemble"
  Abstract: Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.

Connected through hyperedge HE-1 (induced by source paper "taxoexpan self supervised taxonomy expansion with position enhanced graph neural network"):
  with members:
    - "a user centered concept mining system for query and document understanding at tencent"
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "position aware graph neural networks"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"

Connected through hyperedge HE-2 (induced by source paper "guiding corpus based set expansion by auxiliary sets generation and co expansion"):
  with members:
    - "discriminative topic mining via category name guided text embedding"
    - "analogies explained towards understanding word embeddings"
    - "fuse multi faceted set expansion by coherent clustering of skip grams"
    - "term set expansion based nlp architect by intel ai lab"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"
    - "stochastic canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "discovering hypernymy in text rich heterogeneous information network by exploiting context granularity"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "scoring lexical entailment with a supervised directional similarity network"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "term definitions help hypernymy detection"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"

Connected through hyperedge HE-5 (induced by source paper "watset local global graph clustering with applications in sense and frame induction"):
  with members:
    - "deep contextualized word representations"
    - "unsupervised semantic frame induction using triclustering"
    - "a framework for enriching lexical semantic resources with distributional semantics"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "improving hypernymy extraction with distributional semantic classes"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "human and machine judgements for russian semantic relatedness"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-6 (induced by source paper "case context aware semantic expansion"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "neural architectures for fine grained entity type classification"
    - "effective approaches to attention based neural machine translation"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "convolutional neural network architectures for matching natural language sentences"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "multi context term embeddings the use case of corpus based term set expansion"):
  with members:
    - "term set expansion based nlp architect by intel ai lab"
    - "term set expansion based on multi context term embeddings an end to end workflow"
    - "distributional term set expansion"
    - "a deeper look into dependency based word embeddings"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-8 (induced by source paper "hiexpan task guided taxonomy construction by hierarchical tree expansion"):
  with members:
    - "end to end reinforcement learning for automatic taxonomy induction"
    - "metapad meta pattern discovery from massive text corpora"
    - "taxonomy induction using hypernym subsequences"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "automated phrase mining from massive text corpora"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"

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)