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: "knowledge base completion baselines strike back"
  Abstract: Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline - our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported.

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 "knowledge graph embedding for link prediction a comparative analysis"):
  with members:
    - "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "efficient probabilistic logic reasoning with graph neural networks"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "provably powerful graph networks"
    - "probabilistic logic neural networks for reasoning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "one shot relational learning for knowledge graphs"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-4 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-5 (induced by source paper "probabilistic logic neural networks for reasoning"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "improving knowledge graph embedding using simple constraints"
    - "learning human object interactions by graph parsing neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-6 (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-7 (induced by source paper "relation embedding with dihedral group in knowledge graph"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "toruse knowledge graph embedding on a lie group"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-8 (induced by source paper "multi step entity centric information retrieval for multi hop question answering"):
  with members:
    - "passage re ranking with bert"
    - "investigating the successes and failures of bert for passage re ranking"
    - "a deep look into neural ranking models for information retrieval"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "entity duet neural ranking understanding the role of knowledge graph semantics in neural information retrieval"
    - "the web as a knowledge base for answering complex questions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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.LG(Machine Learning)