human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning attention based embeddings for relation prediction in knowledge graphs"):
  Abstract: The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multihop relations in our model. Our empirical study offers insights into the efficacy of our attention based model and we show marked performance gains in comparison to state of the art methods on all datasets.

Includes members:
  Member "multi hop knowledge graph reasoning with reward shaping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "a novel embedding model for knowledge base completion based on convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crwiz a framework for crowdsourcing real time wizard of oz dialogues")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deeppath a reinforcement learning method for knowledge graph reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "convolutional 2d knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "question answering over knowledge base with neural attention combining global knowledge information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs")
  Member "complex embeddings for simple link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")

Question: Please tell me which class the source paper 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)