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: "cross temporal recurrent networks for ranking question answer pairs"
  Abstract: Temporal gates play a significant role in modern recurrent-based neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory (LSTM), temporal gates control the amount of information retained or discarded over time, not only playing an important role in influencing the learned representations but also serving as a protection against vanishing gradients. This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner. In our approach, temporal gates are learned via 1D convolutional layers and then subsequently cross applied across question and answer for joint learning. Empirically, we show that this conceptually simple sharing of temporal gates can lead to competitive performance across multiple benchmarks. Intuitively, what our network achieves can be interpreted as learning representations of question and answer pairs that are aware of what each other is remembering or forgetting, i.e., pairwise temporal gating. Via extensive experiments, we show that our proposed model achieves state-of-the-art performance on two community-based QA datasets and competitive performance on one factoid-based QA dataset.

Connected through hyperedge HE-1 (induced by source paper "gated group self attention for answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "self attentional acoustic models"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "non local neural networks"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "deep semantic role labeling with self attention"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "hashing based answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "supervised and unsupervised transfer learning for question answering"
    - "neural domain adaptation for biomedical question answering"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"

Connected through hyperedge HE-3 (induced by source paper "antnet deep answer understanding network for natural reverse qa"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "semi interactive attention network for answer understanding in reverse qa"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning to ask neural question generation for reading comprehension"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-4 (induced by source paper "co stack residual affinity networks with multi level attention refinement for matching text sequences"):
  with members:
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a continuously growing dataset of sentential paraphrases"
    - "natural language inference over interaction space"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "multi cast attention networks for retrieval based question answering and response prediction"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "multi pointer co attention networks for recommendation"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "neural ranking models with weak supervision"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "word entity duet representations for document ranking"

Connected through hyperedge HE-6 (induced by source paper "multi range reasoning for machine comprehension"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "reinforced mnemonic reader for machine comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "couplenet paying attention to couples with coupled attention for relationship recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "multi pointer co attention networks for recommendation"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "25 tweets to know you a new model to predict personality with social media"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "multi task learning with multi view attention for answer selection and knowledge base question answering"):
  with members:
    - "meta multi task learning for sequence modeling"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "improved neural relation detection for knowledge base question answering"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "attentive pooling networks"
    - "squad 100 000 questions for machine comprehension of text"

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)