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: "adaptive convolutional filter generation for natural language understanding"
  Abstract: Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP are not expressive enough, in the sense that all input sentences share the same learned (and static) set of filters. Motivated by this problem, we propose an adaptive convolutional filter generation framework for natural language understanding, by leveraging a meta network to generate input-aware filters. We further generalize our framework to model question-answer sentence pairs and propose an adaptive question answering (AdaQA) model; a novel two-way feature abstraction mechanism is introduced to encapsulate co-dependent sentence representations. We investigate the effectiveness of our framework on document categorization and answer sentence-selection tasks, achieving state-of-the-art performance on several benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "nash toward end to end neural architecture for generative semantic hashing"):
  with members:
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "deep supervised discrete hashing"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "stochastic generative hashing"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "deconvolutional paragraph representation learning"

Connected through hyperedge HE-2 (induced by source paper "semantic sentence matching with densely connected recurrent and co attentive information"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "natural language inference over interaction space"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-3 (induced by source paper "baseline needs more love on simple word embedding based models and associated pooling mechanisms"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "deconvolutional latent variable model for text sequence matching"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "deconvolutional paragraph representation learning"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"

Connected through hyperedge HE-4 (induced by source paper "deconvolutional latent variable model for text sequence matching"):
  with members:
    - "zero shot learning via class conditioned deep generative models"
    - "adversarially regularized autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "deconvolutional paragraph representation learning"
    - "bilateral multi perspective matching for natural language sentences"
    - "variational neural machine translation"
    - "a compare aggregate model for matching text sequences"

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)