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: "tf ranking scalable tensorflow library for learning to rank"
  Abstract: Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization and question answering. While there is widespread support for classification and regression based learning, support for learning-to-rank in deep learning has been limited. We introduce TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. TensorFlow Ranking has been deployed in production systems within Google; it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data, which can include heterogeneous dense and sparse features. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. We also show that ranking models built using our model scale well for distributed training, without significant impact on metrics. The proposed library is available to the open source community, with the hope that it facilitates further academic research and industrial applications in the field of learning-to-rank.

Connected through hyperedge HE-1 (induced by source paper "separate and attend in personal email search"):
  with members:
    - "spherical text embedding"
    - "area attention"
    - "domain adaptation for enterprise email search"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-2 (induced by source paper "listwise learning to rank by exploring unique ratings"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "a deep look into neural ranking models for information retrieval"
    - "learning a deep listwise context model for ranking refinement"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "applying deep learning to airbnb search"
    - "novel approaches to accelerating the convergence rate of markov decision process for search result diversification"
    - "learning groupwise multivariate scoring functions using deep neural networks"
    - "unbiased lambdamart an unbiased pairwise learning to rank algorithm"

Connected through hyperedge HE-3 (induced by source paper "automatic compilation of resources for academic writing and evaluating with informal word identification and paraphrasing system"):
  with members:
    - "a report on the complex word identification shared task 2018"
    - "par4sim adaptive paraphrasing for text simplification"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "dynamic multi level multi task learning for sentence simplification"
    - "learning to paraphrase for question answering"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "domain adaptation for enterprise email search"):
  with members:
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "neural ranking models with weak supervision"
    - "adversarial discriminative domain adaptation"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"
    - "neural models for information retrieval"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "self attentive document interaction networks for permutation equivariant ranking"):
  with members:
    - "a deep look into neural ranking models for information retrieval"
    - "learning a deep listwise context model for ranking refinement"
    - "attention based deep multiple instance learning"
    - "learning groupwise multivariate scoring functions using deep neural networks"
    - "deep sets"
    - "seq2slate re ranking and slate optimization with rnns"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "unigrasp learning a unified model to grasp with n fingered robotic hands"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"
    - "modeling grasp motor imagery through deep conditional generative models"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a point set generation network for 3d object reconstruction from a single image"

Connected through hyperedge HE-7 (induced by source paper "learning groupwise scoring functions using deep neural networks"):
  with members:
    - "learning a deep listwise context model for ranking refinement"
    - "seq2slate re ranking and slate optimization with rnns"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "neural ranking models with weak supervision"
    - "unbiased learning to rank with biased feedback"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a study of matchpyramid models on ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-8 (induced by source paper "matchzoo a learning practicing and developing system for neural text matching"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "matchzoo a toolkit for deep text matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "text matching as image recognition"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a deep architecture for semantic matching with multiple positional sentence representations"

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.IR(Information Retrieval)