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: "t exponential triplet embedding"
  Abstract: Given a set of relative similarities between objects in the form of triplets "object i is more similar to object j than to object k", we consider the problem of finding an embedding of these objects in a metric space. This problem is generally referred to as triplet embedding. Our main focus in this paper is the case where a subset of triplets are corrupted by noise, such that the order of objects in a triple is reversed. In a crowdsourcing application, for instance, this noise may arise due to varying skill levels or different opinions of the human evaluators. As we show, all existing triplet embedding methods fail to handle even low levels of noise. Inspired by recent advances in robust binary classification and ranking, we introduce a new technique, called t-Exponential Triplet Embedding (t-ETE), that produces high-quality embeddings even in the presence of significant amount of noise in the triplets. By an extensive set of experiments on both synthetic and real-world datasets, we show that our method outperforms all the other methods, giving rise to new insights on real-world data, which have been impossible to observe using the previous techniques.

Connected through hyperedge HE-1 (induced by source paper "efficient data analytics on augmented similarity triplets"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "efficient online relative comparison kernel learning"
    - "cost effective hits for relative similarity comparisons"
    - "adaptively learning the crowd kernel"
    - "fast set intersection and two patterns matching"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "crowdsourced correlation clustering with relative distance comparisons"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "cost effective hits for relative similarity comparisons"
    - "crowdsourcing a word emotion association lexicon"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-3 (induced by source paper "two temperature logistic regression based on the tsallis divergence"):
  with members:
    - "minimizing the misclassification error rate using a surrogate convex loss"

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)