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 "lexical features are more vulnerable syntactic features have more predictive power"):
  Abstract: Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models. In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations. The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications. We show that lexical features are more sensitive to text modifications than syntactic ones. However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features. Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both conversational and written language.

Includes members:
  Member "evaluating the state of the art of end to end natural language generation the e2e nlg challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metaphoric paraphrase generation")
  Member "impact of asr on alzheimer s disease detection all errors are equal but deletions are more equal than others":
    also connected through other hyperedges:
      (none)
  Member "isolating effects of age with fair representation learning when assessing dementia":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a multi modal feature embedding approach to diagnose alzheimer disease from spoken language")
  Member "controlling personality based stylistic variation with neural natural language generators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "detecting cognitive impairments by agreeing on interpretations of linguistic features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "current challenges in spoken dialogue systems and why they are critical for those living with dementia")
  Member "generating natural language adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "the effect of heterogeneous data for alzheimer s disease detection from speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variations on the chebyshev lagrange activation function")
  Member "on the importance of normative data in speech based assessment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "variations on the chebyshev lagrange activation function")

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)