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: "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
  Abstract: We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.

Connected through hyperedge HE-1 (induced by source paper "open knowledge enrichment for long tail entities"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "truth inference at scale a bayesian model for adjudicating highly redundant crowd annotations"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"
    - "document filtering for long tail entities"

Connected through hyperedge HE-2 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "adversarial domain adaptation for duplicate question detection"

Connected through hyperedge HE-3 (induced by source paper "improving distant supervised relation extraction by dynamic neural network"):
  with members:
    - "global textual relation embedding for relational understanding"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "cross relation cross bag attention for distantly supervised relation extraction"
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "multi labeled relation extraction with attentive capsule network"
    - "combining distant and direct supervision for neural relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "reinforcement learning for relation classification from noisy data"

Connected through hyperedge HE-4 (induced by source paper "transfer learning for relation extraction via relation gated adversarial learning"):
  with members:
    - "adversarial domain adaptation for duplicate question detection"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "see syntax aware entity embedding for neural relation extraction"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "meta relational learning for few shot link prediction in knowledge graphs"):
  with members:
    - "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"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-6 (induced by source paper "context aware deep model for entity recommendation in search engine at alibaba"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"
    - "adaptive sampled softmax with kernel based sampling"
    - "node2vec scalable feature learning for networks"
    - "querying knowledge graphs by example entity tuples"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "knowledge based biomedical data science 2019"):
  with members:
    - "graph embedding on biomedical networks methods applications and evaluations"
    - "constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns"
    - "medical knowledge embedding based on recursive neural network for multi disease diagnosis"
    - "opa2vec combining formal and informal content of biomedical ontologies to improve similarity based prediction"

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)