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 "stance detection benchmark how robust is your stance detection"):
  Abstract: Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search. However, while stance is easily detected by humans, machine learning models are clearly falling short of this task. Given the major differences in dataset sizes and framing of StD (e.g. number of classes and inputs), we introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning (MDL) setting, as well as from related tasks via transfer learning. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets. Yet, the models still perform well below human capabilities and even simple adversarial attacks severely hurt the performance of MDL models. Deeper investigation into this phenomenon suggests the existence of biases inherited from multiple datasets by design. Our analysis emphasizes the need of focus on robustness and de-biasing strategies in multi-task learning approaches. The benchmark dataset and code is made available.

Includes members:
  Member "don t take the easy way out ensemble based methods for avoiding known dataset biases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "stancy stance classification based on consistency cues":
    also connected through other hyperedges:
      (none)
  Member "multi task deep neural networks for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "seeing things from a different angle discovering diverse perspectives about claims":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "enteval a holistic evaluation benchmark for entity representations")
  Member "unlearn dataset bias in natural language inference by fitting the residual":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial filters of dataset biases")
  Member "combating adversarial misspellings with robust word recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robustness to programmable string transformations via augmented abstract training")
  Member "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial filters of dataset biases")
  Member "sampling bias in deep active classification an empirical study":
    also connected through other hyperedges:
      (none)

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)