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 "evaluating logical generalization in graph neural networks"):
  Abstract: Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner. However, while relational learning algorithms such as graph neural networks (GNNs) show promise, we do not understand how effectively these approaches can adapt to new tasks. In this work, we study the task of logical generalization using GNNs by designing a benchmark suite grounded in first-order logic. Our benchmark suite, GraphLog, requires that learning algorithms perform rule induction in different synthetic logics, represented as knowledge graphs. GraphLog consists of relation prediction tasks on 57 distinct logical domains. We use GraphLog to evaluate GNNs in three different setups: single-task supervised learning, multi-task pretraining, and continual learning. Unlike previous benchmarks, our approach allows us to precisely control the logical relationship between the different tasks. We find that the ability for models to generalize and adapt is strongly determined by the diversity of the logical rules they encounter during training, and our results highlight new challenges for the design of GNN models. We publicly release the dataset and code used to generate and interact with the dataset at https://www.cs.mcgill.ca/~ksinha4/graphlog.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "enhancing the transformer with explicit relational encoding for math problem solving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "are powerful graph neural nets necessary a dissection on graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking graph neural networks")
  Member "clutrr a diagnostic benchmark for inductive reasoning from text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformers as soft reasoners over language")
  Member "continual learning a comparative study on how to defy forgetting in classification tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "graph neural tangent kernel fusing graph neural networks with graph kernels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization")
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "on training recurrent neural networks for lifelong learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "frosting weights for better continual training")

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)