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 "stress test evaluation for natural language inference"):
  Abstract: Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed "stress tests" that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area.

Includes members:
  Member "annotation artifacts in natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "hypothesis only baselines in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases")
  Member "dr bilstm dependent reading bidirectional lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kernel graph attention network for fact verification")
  Member "a challenge set approach to evaluating machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "the repeval 2017 shared task multi genre natural language inference with sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "evaluating discourse phenomena in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward making the most of context in neural machine translation")
  Member "shortcut stacked sentence encoders for multi domain inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "synthetic and natural noise both break neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")

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)