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: "conditional probability tree estimation analysis and algorithms"
  Abstract: We consider the problem of estimating the conditional probability of a label in time O(log n), where n is the number of possible labels. We analyze a natural reduction of this problem to a set of binary regression problems organized in a tree structure, proving a regret bound that scales with the depth of the tree. Motivated by this analysis, we propose the first online algorithm which provably constructs a logarithmic depth tree on the set of labels to solve this problem. We test the algorithm empirically, showing that it works succesfully on a dataset with roughly 106 labels.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for set valued prediction in multi class classification"):
  with members:
    - "billion scale similarity search with gpus"
    - "lshtc a benchmark for large scale text classification"
    - "consistent algorithms for multiclass classification with a reject option"
    - "learning reductions that really work"
    - "very deep convolutional networks for large scale image recognition"
    - "optimizing f measure a tale of two approaches"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-2 (induced by source paper "on the computational complexity of the probabilistic label tree algorithms"):
  with members:
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "distributed representations of words and phrases and their compositionality"
    - "error correcting tournaments"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a no regret generalization of hierarchical softmax to extreme multi label classification"):
  with members:
    - "logarithmic time online multiclass prediction"
    - "fast label embeddings via randomized linear algebra"
    - "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-5 (induced by source paper "efficient loss based decoding on graphs for extreme classification"):
  with members:
    - "log time and log space extreme classification"
    - "logarithmic time online multiclass prediction"
    - "fast exact search in hamming space with multi index hashing"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-6 (induced by source paper "ensembles of nested dichotomies with multiple subset evaluation"):
  with members:
    - "error correcting tournaments"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-7 (induced by source paper "data dependent generalization bounds for multi class classification"):
  with members:
    - "generalization error bounds for learning to rank does the length of document lists matter"
    - "a vector contraction inequality for rademacher complexities"
    - "lshtc a benchmark for large scale text classification"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "efficient large scale video classification"
    - "maximum margin multiclass nearest neighbors"
    - "multiclass learnability and the erm principle"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-8 (induced by source paper "falcon feature driven selective classification for energy efficient image recognition"):
  with members:
    - "wide residual networks"
    - "learning both weights and connections for efficient neural networks"
    - "object detection using semantic decomposition for energy efficient neural computing"

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)