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: "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"
  Abstract: We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates. Built using an extension to the segmental RNN that emphasizes recall, our basic system achieves competitive performance without any calls to a syntactic parser. We then introduce a method that uses phrase-syntactic annotations from the Penn Treebank during training only, through a multitask objective; no parsing is required at training or test time. This "syntactic scaffold" offers a cheaper alternative to traditional syntactic pipelining, and achieves state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "positional attention based frame identification with bert a deep learning approach to target disambiguation and semantic frame selection"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning joint semantic parsers from disjoint data"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "interactive attention networks for aspect level sentiment classification"

Connected through hyperedge HE-2 (induced by source paper "knowledge enriched visual storytelling"):
  with members:
    - "visual story post editing"
    - "positional encoding to control output sequence length"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "vasta a vision and language assisted smartphone task automation system"):
  with members:
    - "statelens a reverse engineering solution for making existing dynamic touchscreens accessible"
    - "pumice a multi modal agent that learns concepts and conditionals from natural language and demonstrations"
    - "yolov3 an incremental improvement"
    - "universal sentence encoder"
    - "universal semantic parsing"
    - "focal loss for dense object detection"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-4 (induced by source paper "dixit interactive visual storytelling via term manipulation"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a crowdsourced frame disambiguation corpus with ambiguity"):
  with members:
    - "crowdtruth 2 0 quality metrics for crowdsourcing with disagreement"

Connected through hyperedge HE-6 (induced by source paper "syntactic scaffolds for semantic structures"):
  with members:
    - "learning joint semantic parsers from disjoint data"
    - "multitask parsing across semantic representations"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-7 (induced by source paper "linguistically informed self attention for semantic role labeling"):
  with members:
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "learning structured text representations"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "multi task identification of entities relations and coreference for scientific knowledge graph construction"):
  with members:
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "construction of the literature graph in semantic scholar"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "semi supervised multitask learning for sequence labeling"

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)