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: "learning groupwise scoring functions using deep neural networks"
  Abstract: While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire input document list. This difference leads to the notion of relative relevance between documents in ranking. The majority of the existing learning-to-rank algorithms model such relativity at the loss level using pairwise or listwise loss functions. However, they are restricted to pointwise scoring functions, i.e., the relevance score of a document is computed based on the document itself, regardless of the other documents in the list. In this paper, we overcome this limitation by proposing generalized groupwise scoring functions (GSFs), in which the relevance score of a document is determined jointly by groups of documents in the list. We learn GSFs with a deep neural network architecture, and demonstrate that several representative learning-to-rank algorithms can be modeled as special cases in our framework. We conduct evaluation using the public MSLR-WEB30K dataset, and our experiments show that GSFs lead to significant performance improvements both in a standalone deep learning architecture, or when combined with a state-of-the-art tree-based learning-to-rank algorithm.

Connected through hyperedge HE-1 (induced by source paper "listwise learning to rank by exploring unique ratings"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "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-2 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "learning a deep listwise context model for ranking refinement"

Connected through hyperedge HE-3 (induced by source paper "personalized context aware re ranking for e commerce recommender systems"):
  with members:
    - "learning a deep listwise context model for ranking refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "seq2slate re ranking and slate optimization with rnns"
    - "self attentive sequential recommendation"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-4 (induced by source paper "setrank learning a permutation invariant ranking model for information retrieval"):
  with members:
    - "universal transformers"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "learning a deep listwise context model for ranking refinement"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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-5 (induced by source paper "tf ranking scalable tensorflow library for learning to rank"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "tensorflow serving flexible high performance ml serving"
    - "tensorflow estimators managing simplicity vs flexibility in high level machine learning frameworks"
    - "neural models for information retrieval"
    - "unbiased learning to rank with biased feedback"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "personalized re ranking for recommendation"):
  with members:
    - "globally optimized mutual influence aware ranking in e commerce search"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "seq2slate re ranking and slate optimization with rnns"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "deep recurrent models with fast forward connections for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "seq2slate re ranking and slate optimization with rnns"):
  with members:
    - "universal transformers"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning a deep listwise context model for ranking refinement"
    - "top k off policy correction for a reinforce recommender system"
    - "learning permutations with sinkhorn policy gradient"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "an attention based deep net for learning to rank"

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)