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 "building dynamic knowledge graphs from text based games"):
  Abstract: We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate elementary KG operations. Furthermore, we introduce a new dataset for KG extraction built upon text-based game transitions (over 300k data points). We conduct experiments and discuss the results.

Includes members:
  Member "does order matter an empirical study on generating multiple keyphrases as a sequence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "clutrr a diagnostic benchmark for inductive reasoning from text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating logical generalization in graph neural networks")
  Member "textworld a learning environment for text based games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "building dynamic knowledge graphs from text using machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "playing text adventure games with graph based deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "generating diverse numbers of diverse keyphrases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "keyphrase generation a multi aspect survey")
  Member "one shot relational learning for knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)