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: "wikipedia based semantic interpretation for natural language processing"
  Abstract: Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

Connected through hyperedge HE-1 (induced by source paper "eliminating search intent bias in learning to rank"):
  with members:
    - "context aware image annotation in active learning"
    - "conversational structure aware and context sensitive topic model for online discussions"
    - "knowledge guided text structuring in clinical trials"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-2 (induced by source paper "wikipedia cultural diversity dataset a complete cartography for 300 language editions"):
  with members:
    - "why the world reads wikipedia beyond english speakers"
    - "understanding editing behaviors in multilingual wikipedia"
    - "multilinguals and wikipedia editing"
    - "notes on noise contrastive estimation and negative sampling"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"

Connected through hyperedge HE-3 (induced by source paper "linknbed multi graph representation learning with entity linkage"):
  with members:
    - "representation learning on graphs methods and applications"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "knowledge base completion baselines strike back"
    - "rdf graph alignment with bisimulation"
    - "probabilistic reasoning via deep learning neural association models"
    - "generic statistical relational entity resolution in knowledge graphs"
    - "cross graph learning of multi relational associations"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"

Connected through hyperedge HE-4 (induced by source paper "a trio neural model for dynamic entity relatedness ranking"):
  with members:
    - "attention is all you need"
    - "a deep relevance matching model for ad hoc retrieval"
    - "effective approaches to attention based neural machine translation"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "document embedding with paragraph vectors"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "recursive feature generation for knowledge based learning"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "machine learning with world knowledge the position and survey"):
  with members:
    - "solving hard coreference problems"
    - "a neural knowledge language model"
    - "harnessing deep neural networks with logic rules"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "cross lingual dataless classification for languages with small wikipedia presence"
    - "world knowledge as indirect supervision for document clustering"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "a restricted visual turing test for deep scene and event understanding"

Connected through hyperedge HE-7 (induced by source paper "analogical inference for multi relational embeddings"):
  with members:
    - "cross graph learning of multi relational associations"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "traversing knowledge graphs in vector space"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "learning new facts from knowledge bases with neural tensor networks and semantic word vectors"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "cross lingual dataless classification for languages with small wikipedia presence"):
  with members:
    - "cross lingual models of word embeddings an empirical comparison"
    - "multilingual models for compositional distributed semantics"
    - "polyglot distributed word representations for multilingual nlp"
    - "zero shot learning through cross modal transfer"
    - "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)