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: "logarithmic time online multiclass prediction"
  Abstract: We study the problem of multiclass classification with an extremely large number of classes (k), with the goal of obtaining train and test time complexity logarithmic in the number of classes. We develop top-down tree construction approaches for constructing logarithmic depth trees. On the theoretical front, we formulate a new objective function, which is optimized at each node of the tree and creates dynamic partitions of the data which are both pure (in terms of class labels) and balanced. We demonstrate that under favorable conditions, we can construct logarithmic depth trees that have leaves with low label entropy. However, the objective function at the nodes is challenging to optimize computationally. We address the empirical problem with a new online decision tree construction procedure. Experiments demonstrate that this online algorithm quickly achieves improvement in test error compared to more common logarithmic training time approaches, which makes it a plausible method in computationally constrained large-k applications.

Connected through hyperedge HE-1 (induced by source paper "joint optimization of tree based index and deep model for recommender systems"):
  with members:
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "deep learning based recommender system a survey and new perspectives"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "the case for learned index structures"
    - "neural collaborative filtering"
    - "billion scale similarity search with gpus"
    - "optimized cost per click in taobao display advertising"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "extreme classification in log memory using count min sketch a case study of amazon search with 50m products"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "mesh tensorflow deep learning for supercomputers"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "ldsm logarithm depth streaming multi label decision trees"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "deep extreme multi label learning"
    - "normalized online learning"
    - "large scale multi label learning with missing labels"
    - "the landmark selection method for multiple output prediction"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "lightmc a dynamic and efficient multiclass decomposition algorithm"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "distributed representations of words and phrases and their compositionality"
    - "multi label prediction via compressed sensing"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-5 (induced by source paper "contextual memory trees"):
  with members:
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "prototypical networks for few shot learning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning to remember rare events"
    - "search engine guided non parametric neural machine translation"
    - "one shot learning with memory augmented neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"

Connected through hyperedge HE-6 (induced by source paper "extreme classification in log memory"):
  with members:
    - "log time and log space extreme classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "compressing neural networks with the hashing trick"
    - "deep networks with large output spaces"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label learning with missing labels"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-7 (induced by source paper "a no regret generalization of hierarchical softmax to extreme multi label classification"):
  with members:
    - "fast label embeddings via randomized linear algebra"
    - "conditional probability tree estimation analysis and algorithms"
    - "deep networks with large output spaces"
    - "distributed representations of words and phrases and their compositionality"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "optimizing f measure a tale of two approaches"
    - "error correcting tournaments"

Connected through hyperedge HE-8 (induced by source paper "efficient loss based decoding on graphs for extreme classification"):
  with members:
    - "log time and log space extreme classification"
    - "conditional probability tree estimation analysis and algorithms"
    - "fast exact search in hamming space with multi index hashing"
    - "large scale multi label learning with missing labels"

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)