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: "cost effective hits for relative similarity comparisons"
  Abstract: Similarity comparisons of the form "Is object a more similar to b than to c?" are useful for computer vision and machine learning applications. Unfortunately, an embedding of $n$ points is specified by $n^3$ triplets, making collecting every triplet an expensive task. In noticing this difficulty, other researchers have investigated more intelligent triplet sampling techniques, but they do not study their effectiveness or their potential drawbacks. Although it is important to reduce the number of collected triplets, it is also important to understand how best to display a triplet collection task to a user. In this work we explore an alternative display for collecting triplets and analyze the monetary cost and speed of the display. We propose best practices for creating cost effective human intelligence tasks for collecting triplets. We show that rather than changing the sampling algorithm, simple changes to the crowdsourcing UI can lead to much higher quality embeddings. We also provide a dataset as well as the labels collected from crowd workers.

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"
    - "t exponential triplet embedding"
    - "efficient online relative comparison kernel learning"
    - "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 "metric learning for individual fairness"):
  with members:
    - "online learning with an unknown fairness metric"
    - "probably approximately metric fair learning"
    - "fairness through computationally bounded awareness"
    - "learning from partial correction"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "a survey on metric learning for feature vectors and structured data"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "fast stochastic ordinal embedding with variance reduction and adaptive step size"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "learning concept embeddings with combined human machine expertise"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "learning multi modal similarity"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-4 (induced by source paper "comparison based framework for psychophysics lab versus crowdsourcing"):
  with members:
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-5 (induced by source paper "landmark ordinal embedding"):
  with members:
    - "inferring networks of substitutable and complementary products"

Connected through hyperedge HE-6 (induced by source paper "vision based real estate price estimation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "stacked generative adversarial networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "you only look once unified real time object detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "learning disentangled representations of texts with application to biomedical abstracts"):
  with members:
    - "aspect augmented adversarial networks for domain adaptation"
    - "disentangled representations for manipulation of sentiment in text"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "conditional similarity networks"
    - "disentangled representations in neural models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "skip thought vectors"
    - "neural variational inference for text processing"

Connected through hyperedge HE-8 (induced by source paper "less but better generalization enhancement of ordinal embedding via distributional margin"):
  with members:
    - "optimal margin distribution machine"
    - "learning concept embeddings with combined human machine expertise"
    - "efficient online relative comparison kernel learning"
    - "large margin distribution machine"
    - "adaptively learning the crowd kernel"
    - "on the doubt about margin explanation of boosting"

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.CV(Computer Vision and Pattern Recognition)