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: "direct optimization of ranking measures"
  Abstract: Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.

Connected through hyperedge HE-1 (induced by source paper "rich item recommendations for rich users via gcnn exploiting dynamic and static side information"):
  with members:
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "provable inductive matrix completion"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"

Connected through hyperedge HE-3 (induced by source paper "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"):
  with members:
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"
    - "hashnet deep learning to hash by continuation"
    - "deep cross modal hashing"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "analysis of regression tree fitting algorithms in learning to rank"):
  with members:
    - "robust logitboost and adaptive base class abc logitboost"
    - "fast abc boost for multi class classification"

Connected through hyperedge HE-5 (induced by source paper "direct optimization of f measure for retrieval based personal question answering"):
  with members:
    - "constrained convolutional recurrent networks to improve speech quality with low impact on recognition accuracy"
    - "attention is all you need"
    - "reinforcement learning to adapt speech enhancement to instantaneous input signal quality"
    - "advances in pre training distributed word representations"
    - "bilateral multi perspective matching for natural language sentences"
    - "neural models for information retrieval"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "word2vec applied to recommendation hyperparameters matter"):
  with members:
    - "specializing joint representations for the task of product recommendation"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "item2vec neural item embedding for collaborative filtering"
    - "from word embeddings to item recommendation"
    - "mllib machine learning in apache spark"
    - "e commerce in your inbox product recommendations at scale"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "vse improving visual semantic embeddings with hard negatives"):
  with members:
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "dual attention networks for multimodal reasoning and matching"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "linking image and text with 2 way nets"
    - "measuring machine intelligence through visual question answering"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-8 (induced by source paper "top rank optimization in linear time"):
  with members:
    - "one pass auc optimization"
    - "efficient optimization of performance measures by classifier adaptation"
    - "sparse support vector infinite push"

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)