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 "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"):
  Abstract: Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convolutional filters on 2D reshapings of entity and relation embeddings in order to capture rich interactions between their components. However, the number of interactions that ConvE can capture is limited. In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE. InteractE is based on three key ideas -- feature permutation, a novel feature reshaping, and circular convolution. Through extensive experiments, we find that InteractE outperforms state-of-the-art convolutional link prediction baselines on FB15k-237. Further, InteractE achieves an MRR score that is 9%, 7.5%, and 23% better than ConvE on the FB15k-237, WN18RR and YAGO3-10 datasets respectively. The results validate our central hypothesis -- that increasing feature interaction is beneficial to link prediction performance. We make the source code of InteractE available to encourage reproducible research.

Includes members:
  Member "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "a re evaluation of knowledge graph completion methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")
  Member "cesi canonicalizing open knowledge bases using embeddings and side information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "reside improving distantly supervised neural relation extraction using side information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "omnidirectional cnn for visual place recognition and navigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "end to end structure aware convolutional networks for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion")
  Member "a novel embedding model for knowledge base completion based on convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "kblrn end to end learning of knowledge base representations with latent relational and numerical features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural graph embedding methods for natural language processing")

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)