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: "cesi canonicalizing open knowledge bases using embeddings and side information"
  Abstract: Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large open Knowledge Bases (KBs). The noun phrases (NPs) and relation phrases in such open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of open KBs as clustering over manually-defined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose CESI — a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI’s effectiveness. We plan to make publicly available all the data and code used in the paper.

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"
    - "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"
    - "improving distantly supervised relation extraction using word and entity based attention"

Connected through hyperedge HE-2 (induced by source paper "amused a multi stream vector representation method for use in natural dialogue"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"

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"
    - "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"
    - "kbgan adversarial learning for knowledge graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "canonicalizing knowledge base literals"):
  with members:
    - "colnet embedding the semantics of web tables for column type prediction"
    - "mapping text to knowledge graph entities using multi sense lstms"
    - "type constrained representation learning in knowledge graphs"
    - "quality assessment of linked datasets using probabilistic approximation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "opiec an open information extraction corpus"):
  with members:
    - "open world knowledge graph completion"

Connected through hyperedge HE-6 (induced by source paper "reside improving distantly supervised neural relation extraction using side information"):
  with members:
    - "see syntax aware entity embedding for neural relation extraction"
    - "candis coupled attention driven neural distant supervision"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "noise mitigation for neural entity typing and relation extraction"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "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.IR(Information Retrieval)