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 "dr bilstm dependent reading bidirectional lstm for natural language inference"):
  Abstract: We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.

Includes members:
  Member "natural language inference over interaction space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "textual entailment with structured attentions and composition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "learning natural language inference using bidirectional lstm model and inner attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mhsan multi head self attention network for visual semantic embedding")
  Member "neural semantic encoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "learning natural language inference with lstm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "a large annotated corpus for learning natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")
  Member "ask me anything dynamic memory networks for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mast a memory augmented self supervised tracker")

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)