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 "adapting meta knowledge graph information for multi hop reasoning over few shot relations"):
  Abstract: Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms the current state-of-the-art methods in few-shot scenarios. Our code and datasets can be obtained from this https URL THU-KEG/MetaKGR.

Includes members:
  Member "cognitive graph for multi hop reading comprehension at scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can graph neural networks count substructures")
  Member "dynamically fused graph network for multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do multi hop question answering systems know how to answer the single hop sub questions")
  Member "reinforcewalk learning to walk in graph with monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")
  Member "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "multi hop knowledge graph reasoning with reward shaping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "hotpotqa a dataset for diverse explainable multi hop question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "variational knowledge graph reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamically pruned message passing networks for large scale knowledge graph reasoning")
  Member "one shot relational learning for knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")

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.AI(Artificial Intelligence)