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 "uncertain natural language inference"):
  Abstract: We propose a refinement of Natural Language Inference (NLI), called Uncertain Natural Language Inference (UNLI), that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. Chiefly, we demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a psychologically motivated probabilistic scale, where items even with the same categorical label, e.g., "contradictions" differ in how likely people judge them to be strictly impossible given a premise. We describe two modeling approaches, as direct scalar regression and as learning-to-rank, finding that existing categorically labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the ternary bin assignment employed in current NLI tasks.

Includes members:
  Member "learning to rank for plausible plausibility":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when choosing plausible alternatives clever hans can be clever")
  Member "annotation artifacts in natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases")
  Member "collecting diverse natural language inference problems for sentence representation evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches")
  Member "hypothesis only baselines in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial filters of dataset biases")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "neural models of factuality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches")
  Member "performance impact caused by hidden bias of training data for recognizing textual entailment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial filters of dataset biases")
  Member "efficient online scalar annotation with bounded support":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interactive refinement of cross lingual word embeddings")

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)