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 "rnns as psycholinguistic subjects syntactic state and grammatical dependency"):
  Abstract: Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language. However, it remains poorly understood what grammatical characteristics of natural language they implicitly learn and represent as a consequence of optimizing the language modeling objective. Here we deploy the methods of controlled psycholinguistic experimentation to shed light on to what extent RNN behavior reflects incremental syntactic state and grammatical dependency representations known to characterize human linguistic behavior. We broadly test two publicly available long short-term memory (LSTM) English sequence models, and learn and test a new Japanese LSTM. We demonstrate that these models represent and maintain incremental syntactic state, but that they do not always generalize in the same way as humans. Furthermore, none of our models learn the appropriate grammatical dependency configurations licensing reflexive pronouns or negative polarity items.

Includes members:
  Member "gender bias in coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "deep rnns encode soft hierarchical syntax":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "colorless green recurrent networks dream hierarchically":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "exploring the syntactic abilities of rnns with multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "do latent tree learning models identify meaningful structure in sentences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "towards linguistically generalizable nlp systems a workshop and shared task":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "semantics derived automatically from language corpora contain human like biases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "think locally act globally federated learning with local and global representations")

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)