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 "be consistent improving procedural text comprehension using label consistency"):
  Abstract: Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing throughout the text, and despite recent advances, current systems still struggle with this task. Our approach is to leverage the fact that, for many procedural texts, multiple independent descriptions are readily available, and that predictions from them should be consistent (label consistency). We present a new learning framework that leverages label consistency during training, allowing consistency bias to be built into the model. Evaluation on a standard benchmark dataset for procedural text, ProPara (Dalvi et al., 2018), shows that our approach significantly improves prediction performance (F1) over prior state-of-the-art systems.

Includes members:
  Member "tracking discrete and continuous entity state for process understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective use of transformer networks for entity tracking")
  Member "building dynamic knowledge graphs from text using machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "allennlp a deep semantic natural language processing platform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "reasoning about actions and state changes by injecting commonsense knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches")
  Member "text2shape generating shapes from natural language by learning joint embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "simulating action dynamics with neural process networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recipegpt generative pre training based cooking recipe generation and evaluation system")
  Member "a thorough examination of the cnn daily mail reading comprehension task":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a study of the tasks and models in machine reading comprehension")

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)