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: "paris probabilistic alignment of relations instances and schema"
  Abstract: One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world's largest ontologies.

Connected through hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs"):
  with members:
    - "a survey on knowledge graphs representation acquisition and applications"
    - "knowledge graph embedding for link prediction a comparative analysis"
    - "multi view knowledge graph embedding for entity alignment"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "jointly learning entity and relation representations for entity alignment"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "learning to exploit long term relational dependencies in knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "crowdsourced collective entity resolution with relational match propagation"):
  with members:
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "principled graph matching algorithms for integrating multiple data sources"
    - "lazier than lazy greedy"
    - "leveraging transitive relations for crowdsourced joins"
    - "sigma simple greedy matching for aligning large knowledge bases"
    - "crowder crowdsourcing entity resolution"
    - "large scale collective entity matching"

Connected through hyperedge HE-3 (induced by source paper "knowledge graph based methods for record linkage"):
  with members:
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "a hierarchical graphical model for record linkage"

Connected through hyperedge HE-4 (induced by source paper "uncovering hidden semantics of set information in knowledge bases"):
  with members:
    - "cardinal virtues extracting relation cardinalities from text"
    - "conjunctive query answering for the description logic shiq"

Connected through hyperedge HE-5 (induced by source paper "learning to exploit long term relational dependencies in knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "representation learning on graphs with jumping knowledge networks"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "end to end entity resolution for big data a survey"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "a survey of blocking and filtering techniques for entity resolution"
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "pigeonring a principle for faster thresholded similarity search"
    - "reuse and adaptation for entity resolution through transfer learning"
    - "snorkel rapid training data creation with weak supervision"
    - "distributed holistic clustering on linked data"
    - "a dnf blocking scheme learner for heterogeneous datasets"

Connected through hyperedge HE-7 (induced by source paper "jointly learning entity and relation representations for entity alignment"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "semi supervised user geolocation via graph convolutional networks"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "adversarial training for multi context joint entity and relation extraction"
    - "variational reasoning for question answering with knowledge graph"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "enriching medcial terminology knowledge bases via pre trained language model and graph convolutional network"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "ernie enhanced language representation with informative entities"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "cross lingual entity alignment via joint attribute preserving embedding"

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.DB(Databases)