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 world knowledge graph completion"
  Abstract: Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an open-world KGC model called ConMask. This model learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationship-dependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task.

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 "open knowledge enrichment for long tail entities"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "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"
    - "convolutional 2d knowledge graph embeddings"
    - "document filtering for long tail entities"

Connected through hyperedge HE-3 (induced by source paper "dynamic parameter allocation in parameter servers"):
  with members:
    - "priority based parameter propagation for distributed dnn training"
    - "deep contextualized word representations"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "analogical inference for multi relational embeddings"
    - "holographic embeddings of knowledge graphs"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "lifelong and interactive learning of factual knowledge in dialogues"):
  with members:
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "listen interact and talk learning to speak via interaction"
    - "towards a continuous knowledge learning engine for chatbots"
    - "one shot relational learning for knowledge graphs"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "naturalizing a programming language via interactive learning"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "leveraging semantics for incremental learning in multi relational embeddings"):
  with members:
    - "robocse robot common sense embedding"
    - "continual lifelong learning with neural networks a review"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "continuous learning in single incremental task scenarios"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "deep generative dual memory network for continual learning"
    - "memory efficient experience replay for streaming learning"
    - "progressive neural networks"

Connected through hyperedge HE-6 (induced by source paper "tackling long tailed relations and uncommon entities in knowledge graph completion"):
  with members:
    - "on first order meta learning algorithms"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "low shot learning from imaginary data"
    - "one shot relational learning for knowledge graphs"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "adapting meta knowledge graph information for multi hop reasoning over few shot relations"):
  with members:
    - "cognitive graph for multi hop reading comprehension at scale"
    - "dynamically fused graph network for multi hop reasoning"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "variational knowledge graph reasoning"
    - "one shot relational learning for knowledge graphs"

Connected through hyperedge HE-8 (induced by source paper "collaborative policy learning for open knowledge graph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on constrained open world probabilistic databases"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "convolutional 2d knowledge graph embeddings"
    - "joint representation learning of text and knowledge for knowledge graph completion"

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)