human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "task specific attentive pooling of phrase alignments contributes to sentence matching"
  Abstract: This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS. The key to reach this observation lies in phrase detection, phrase representation, phrase alignment, and more importantly how to connect those aligned phrases of different matching degrees with the final classifier. Prior work (i) has limitations in phrase generation and representation, or (ii) conducts alignment at word and phrase levels by handcrafted features or (iii) utilizes a single framework of alignment without considering the characteristics of specific tasks, which limits the framework's effectiveness across tasks. We propose an architecture based on Gated Recurrent Unit that supports (i) representation learning of phrases of arbitrary granularity and (ii) task-specific attentive pooling of phrase alignments between two sentences. Experimental results on TE and AS match our observation and show the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "monalog a lightweight system for natural language inference based on monotonicity"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "probing natural language inference models through semantic fragments"
    - "annotation artifacts in natural language inference data"
    - "acquisition of phrase correspondences using natural deduction proofs"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stress test evaluation for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "regmapr text matching made easy"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "enhancing sentence embedding with generalized pooling"
    - "natural language inference over interaction space"
    - "on the state of the art of evaluation in neural language models"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "shortcut stacked sentence encoders for multi domain inference"

Connected through hyperedge HE-3 (induced by source paper "end task oriented textual entailment via deep explorations of inter sentence interactions"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "deep contextualized word representations"
    - "bilateral multi perspective matching for natural language sentences"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-4 (induced by source paper "acquisition of phrase correspondences using natural deduction proofs"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "a ccg parsing with a supertag and dependency factored model"
    - "determining semantic textual similarity using natural deduction proofs"
    - "learning semantically and additively compositional distributional representations"
    - "representing meaning with a combination of logical and distributional models"
    - "a large annotated corpus for learning natural language inference"

Question: Please tell me which class the center node 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)