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: "a theoretical analysis of ndcg type ranking measures"
  Abstract: A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there are extensive empirical studies of NDCG, little is known about its theoretical properties. We first show that, whatever the ranking function is, the standard NDCG which adopts a logarithmic discount, converges to 1 as the number of items to rank goes to infinity. On the first sight, this result is very surprising. It seems to imply that NDCG cannot differentiate good and bad ranking functions, contradicting to the empirical success of NDCG in many applications. In order to have a deeper understanding of ranking measures in general, we propose a notion referred to as consistent distinguishability. This notion captures the intuition that a ranking measure should have such a property: For every pair of substantially different ranking functions, the ranking measure can decide which one is better in a consistent manner on almost all datasets. We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions. We next characterize the set of all feasible discount functions for NDCG according to the concept of consistent distinguishability. Specifically we show that whether NDCG has consistent distinguishability depends on how fast the discount decays, and 1/r is a critical point. We then turn to the cut-off version of NDCG, i.e., NDCG@k. We analyze the distinguishability of NDCG@k for various choices of k and the discount functions. Experimental results on real Web search datasets agree well with the theory.

Connected through hyperedge HE-1 (induced by source paper "targeted display advertising the case of preferential attachment"):
  with members:
    - "adaptive matrix completion for the users and the items in tail"
    - "intent term selection and refinement in e commerce queries"
    - "deep visual domain adaptation a survey"
    - "field weighted factorization machines for click through rate prediction in display advertising"
    - "unified deep supervised domain adaptation and generalization"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "extreme algorithm selection with dyadic feature representation"):
  with members:
    - "automated algorithm selection survey and perspectives"
    - "aslib a benchmark library for algorithm selection"
    - "openml networked science in machine learning"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "restart strategy selection using machine learning techniques"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-3 (induced by source paper "extreme regression for dynamic search advertising"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"
    - "fast label embeddings for extremely large output spaces"
    - "lshtc a benchmark for large scale text classification"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "image question answer synergistic network for visual dialog"):
  with members:
    - "bilinear attention networks"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "modulating early visual processing by language"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "learning visual reasoning without strong priors"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"

Connected through hyperedge HE-5 (induced by source paper "large scale collaborative filtering with product embeddings"):
  with members:
    - "deep sets"
    - "structured attention networks"
    - "starspace embed all the things"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "deep cross network for ad click predictions"
    - "specializing joint representations for the task of product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "divgraphpointer a graph pointer network for extracting diverse keyphrases"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "deep keyphrase generation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "convolutional sequence to sequence learning"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "applications of generative adversarial models in visual search reformulation"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "web scale responsive visual search at bing"
    - "attgan facial attribute editing by only changing what you want"
    - "triangle generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "visual search at ebay"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual discovery at pinterest"

Connected through hyperedge HE-8 (induced by source paper "adaptive nonnegative matrix factorization and measure comparisons for recommender systems"):
  with members:
    - "eigenrec generalizing puresvd for effective and efficient top n recommendations"
    - "pagerank beyond the web"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

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.LG(Machine Learning)