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: "solving multiclass learning problems via error correcting output codes"
  Abstract: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k &gt 2 values (i.e., k ``classes''). The definition is acquired by studying collections of training examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that---like the other methods---the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.

Connected through hyperedge HE-1 (induced by source paper "crowd scene analysis by output encoding"):
  with members:
    - "point in box out beyond counting persons in crowds"
    - "centernet keypoint triplets for object detection"
    - "selective kernel networks"
    - "scale aware trident networks for object detection"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "where are the blobs counting by localization with point supervision"
    - "top down feedback for crowd counting convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "a tutorial on ensembles and deep learning fusion with mnist as guiding thread a complex heterogeneous fusion scheme reaching 10 digits error"):
  with members:
    - "path capsule networks"
    - "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"
    - "dynamic routing between capsules"
    - "recurrent neural network regularization"
    - "representation learning a review and new perspectives"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-3 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep learning for sentiment analysis a survey"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-4 (induced by source paper "predictive ensemble learning with application to scene text detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "textboxes a single shot oriented scene text detector"
    - "fusion of an ensemble of augmented image detectors for robust object detection"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "rank of experts detection network ensemble"

Connected through hyperedge HE-5 (induced by source paper "error correcting neural network"):
  with members:
    - "a mean field theory of batch normalization"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "batch normalization is a cause of adversarial vulnerability"
    - "adversarial attacks and defences competition"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "magnet a two pronged defense against adversarial examples"
    - "sequential multi class labeling in crowdsourcing"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-6 (induced by source paper "superbloom bloom filter meets transformer"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "next item recommendation with self attention"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hash embeddings for efficient word representations"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-7 (induced by source paper "mute data similarity driven multi hot target encoding for neural network design"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "beyond one hot encoding lower dimensional target embedding"
    - "datasheets for datasets"
    - "on the limitation of convolutional neural networks in recognizing negative images"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "label embedding for image classification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "introducing graph smoothness loss for training deep learning architectures"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "in defense of the triplet loss for person re identification"
    - "mixup beyond empirical risk minimization"
    - "regularizing neural networks by penalizing confident output distributions"
    - "identity mappings in deep residual networks"
    - "facenet a unified embedding for face recognition and clustering"

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)