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: "sigma simple greedy matching for aligning large knowledge bases"
  Abstract: The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.

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"
    - "crowder crowdsourcing entity resolution"
    - "large scale collective entity matching"
    - "paris probabilistic alignment of relations instances and schema"

Connected through hyperedge HE-3 (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-4 (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-5 (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-6 (induced by source paper "rlink deep reinforcement learning for user identity linkage"):
  with members:
    - "deep reinforcement learning for page wise recommendations"
    - "adaptive pattern matching with reinforcement learning for dynamic graphs"
    - "learning transferable architectures for scalable image recognition"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "node2vec scalable feature learning for networks"
    - "neural architectures for named entity recognition"
    - "effective approaches to attention based neural machine translation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "towards interpretable and learnable risk analysis for entity resolution"):
  with members:
    - "r humo a risk aware human machine cooperation framework for entity resolution with quality guarantees"
    - "deep anomaly detection with outlier exposure"
    - "discriminative active learning"
    - "on calibration of modern neural networks"
    - "holoclean holistic data repairs with probabilistic inference"
    - "concrete problems in ai safety"
    - "why should i trust you explaining the predictions of any classifier"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-8 (induced by source paper "mmkg multi modal knowledge graphs"):
  with members:
    - "embedding multimodal relational data for knowledge base completion"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "a neural knowledge language model"
    - "the more you know using knowledge graphs for image classification"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "adam a method for stochastic optimization"

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.AI(Artificial Intelligence)