human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "clutrr a diagnostic benchmark for inductive reasoning from text"
  Abstract: The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systematicity of NLU systems. Motivated by classic work on inductive logic programming, CLUTRR requires that an NLU system infer kinship relations between characters in short stories. Successful performance on this task requires both extracting relationships between entities, as well as inferring the logical rules governing these relationships. CLUTRR allows us to precisely measure a model's ability for systematic generalization by evaluating on held-out combinations of logical rules, and it allows us to evaluate a model's robustness by adding curated noise facts. Our empirical results highlight a substantial performance gap between state-of-the-art NLU models (e.g., BERT and MAC) and a graph neural network model that works directly with symbolic inputs---with the graph-based model exhibiting both stronger generalization and greater robustness.

Connected through hyperedge HE-1 (induced by source paper "evaluating logical generalization in graph neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "pytorch an imperative style high performance deep learning library"
    - "on training recurrent neural networks for lifelong learning"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "transformers as soft reasoners over language"):
  with members:
    - "differentiable reasoning on large knowledge bases and natural language"
    - "learning deep transformer models for machine translation"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "probing natural language inference models through semantic fragments"
    - "improving graph neural network representations of logical formulae with subgraph pooling"
    - "analysing mathematical reasoning abilities of neural models"
    - "establishing strong baselines for the new decade sequence tagging syntactic and semantic parsing with bert"
    - "quartz an open domain dataset of qualitative relationship questions"

Connected through hyperedge HE-3 (induced by source paper "inductive relation prediction by subgraph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "link prediction based on graph neural networks"
    - "logic attention based neighborhood aggregation for inductive knowledge graph embedding"
    - "how powerful are graph neural networks"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-4 (induced by source paper "exploring structural inductive biases in emergent communication"):
  with members:
    - "on the interaction between supervision and self play in emergent communication"
    - "measuring compositionality in representation learning"
    - "ease of teaching and language structure from emergent communication"
    - "egg a toolkit for research on emergence of language in games"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-5 (induced by source paper "building dynamic knowledge graphs from text based games"):
  with members:
    - "does order matter an empirical study on generating multiple keyphrases as a sequence"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "playing text adventure games with graph based deep reinforcement learning"
    - "generating diverse numbers of diverse keyphrases"
    - "one shot relational learning for knowledge graphs"
    - "attention is all you need"
    - "pointing the unknown words"

Question: Please tell me which class the center node 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)