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: "solving verbal comprehension questions in iq test by knowledge powered word embedding"
  Abstract: Intelligence Quotient (IQ) Test is a set of standardized questions designed to evaluate human intelligence. Verbal comprehension questions appear very frequently in IQ tests, which measure human's verbal ability including the understanding of the words with multiple senses, the synonyms and antonyms, and the analogies among words. In this work, we explore whether such tests can be solved automatically by artificial intelligence technologies, especially the deep learning technologies that are recently developed and successfully applied in a number of fields. However, we found that the task was quite challenging, and simply applying existing technologies (e.g., word embedding) could not achieve a good performance, mainly due to the multiple senses of words and the complex relations among words. To tackle these challenges, we propose a novel framework consisting of three components. First, we build a classifier to recognize the specific type of a verbal question (e.g., analogy, classification, synonym, or antonym). Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries. Third, for each type of questions, we propose a specific solver based on the obtained distributed word representations and relation representations. Experimental results have shown that the proposed framework can not only outperform existing methods for solving verbal comprehension questions but also exceed the average performance of the Amazon Mechanical Turk workers involved in the study. The results indicate that with appropriate uses of the deep learning technologies we might be a further step closer to the human intelligence.

Connected through hyperedge HE-1 (induced by source paper "mining entity synonyms with efficient neural set generation"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "deep sets"
    - "automatic synonym discovery with knowledge bases"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "learning to cluster in order to transfer across domains and tasks"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-2 (induced by source paper "automatic synonym discovery with knowledge bases"):
  with members:
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "distributed representations of words and phrases and their compositionality"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-3 (induced by source paper "iq of neural networks"):
  with members:
    - "the microsoft 2016 conversational speech recognition system"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "action conditional video prediction using deep networks in atari games"
    - "visual learning of arithmetic operations"
    - "anticipating visual representations from unlabeled video"
    - "deep multi scale video prediction beyond mean square error"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "contextual rnn gans for abstract reasoning diagram generation"):
  with members:
    - "generating images with recurrent adversarial networks"
    - "generative image modeling using style and structure adversarial networks"
    - "generative adversarial text to image synthesis"
    - "context encoders feature learning by inpainting"
    - "generating videos with scene dynamics"
    - "generative visual manipulation on the natural image manifold"
    - "spatio temporal video autoencoder with differentiable memory"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-5 (induced by source paper "measuring an artificial intelligence system s performance on a verbal iq test for young children"):
  with members:
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "commonsense reasoning and large network analysis a computational study of conceptnet 4"
    - "analogy perception applied to seven tests of word comprehension"
    - "tests of machine intelligence"

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)