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: "open question answering with weakly supervised embedding models"
  Abstract: Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data.

Connected through hyperedge HE-1 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "bidirectional attentive memory networks for question answering over knowledge bases"):
  with members:
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "universal semantic parsing"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "bidirectional attention flow for machine comprehension"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-3 (induced by source paper "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "a re evaluation of knowledge graph completion methods"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"

Connected through hyperedge HE-4 (induced by source paper "meta relational learning for few shot link prediction in knowledge graphs"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "one shot relational learning for knowledge graphs"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "convolutional 2d knowledge graph embeddings"
    - "matching networks for one shot learning"

Connected through hyperedge HE-5 (induced by source paper "a general fofe net framework for simple and effective question answering over knowledge bases"):
  with members:
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "neural networks models for entity discovery and linking"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "large scale simple question answering with memory networks"

Connected through hyperedge HE-6 (induced by source paper "structured query construction via knowledge graph embedding"):
  with members:
    - "scef a support confidence aware embedding framework for knowledge graph refinement"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "keyword search on rdf graphs a query graph assembly approach"
    - "open world knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "question answering with subgraph embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "secret semantically enhanced classification of real world tasks"):
  with members:
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "muse modularizing unsupervised sense embeddings"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep visual semantic alignments for generating image descriptions"
    - "zero shot learning through cross modal transfer"
    - "efficient estimation of word representations in vector space"
    - "word emdeddings through hellinger pca"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-8 (induced by source paper "disentangling latent emotions of word embeddings on complex emotional narratives"):
  with members:
    - "attending to emotional narratives"
    - "non autoregressive neural machine translation"
    - "visualizing and understanding neural models in nlp"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "efficient estimation of word representations in vector space"

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)