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 "relation embedding with dihedral group in knowledge graph"):
  Abstract: Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relations such that the bilinear form defined therein is a well-behaved scoring function. Despite of their successful performances, existing bilinear forms overlook the modeling of relation compositions, resulting in lacks of interpretability for reasoning on KG. To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. Furthermore, our approach models the relation embeddings parametrized by discrete values, thereby decrease the solution space drastically. Our experiments show that DihEdral is able to capture all desired properties such as (skew-) symmetry, inversion and (non-) Abelian composition, and outperforms existing bilinear form based approach and is comparable to or better than deep learning models such as ConvE.

Includes members:
  Member "understanding straight through estimator in training activation quantized neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "learning to exploit long term relational dependencies in knowledge graphs":
    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 "on the equivalence of holographic and complex embeddings for link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "autokge searching scoring functions for knowledge graph embedding")
  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 "toruse knowledge graph embedding on a lie group":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "knowledge base completion baselines strike back":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")

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.CL(Computation and Language)