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: "error correcting tournaments"
  Abstract: We present a family of pairwise tournaments reducing $k$-class classification to binary classification. These reductions are provably robust against a constant fraction of binary errors. The results improve on the PECOC construction \cite{SECOC} with an exponential improvement in computation, from $O(k)$ to $O(\log_2 k)$, and the removal of a square root in the regret dependence, matching the best possible computation and regret up to a constant.

Connected through hyperedge HE-1 (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"
    - "conditional probability tree estimation analysis and algorithms"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "on oracle efficient pac rl with rich observations"):
  with members:
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "a pac rl algorithm for episodic pomdps"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"
    - "learning to search better than your teacher"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "ensembles of nested dichotomies with multiple subset evaluation"):
  with members:
    - "conditional probability tree estimation analysis and algorithms"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-5 (induced by source paper "on the boosting ability of top down decision tree learning algorithm for multiclass classification"):
  with members:
    - "conditional probability tree estimation analysis and algorithms"

Connected through hyperedge HE-6 (induced by source paper "learning reductions that really work"):
  with members:
    - "learning to search better than your teacher"
    - "logarithmic time online multiclass prediction"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient programmable learning to search"
    - "least squares revisited scalable approaches for multi class prediction"
    - "normalized online learning"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "output space search for structured prediction"

Connected through hyperedge HE-7 (induced by source paper "reinforcement and imitation learning via interactive no regret learning"):
  with members:
    - "approximate policy iteration schemes a comparison"
    - "learning policies for contextual submodular prediction"
    - "the interplay between stability and regret in online learning"
    - "decoupling exploration and exploitation in multi armed bandits"
    - "projection free online learning"
    - "stability conditions for online learnability"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "logarithmic time online multiclass prediction"):
  with members:
    - "conditional probability tree estimation analysis and algorithms"
    - "least squares revisited scalable approaches for multi class prediction"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

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.AI(Artificial Intelligence)