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: "neural network based clustering using pairwise constraints"
  Abstract: This paper presents a neural network-based end-to-end clustering framework. We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering. The network is trained with weak labels, specifically partial pairwise relationships between data instances. The cluster assignments and their probabilities are then obtained at the output layer by feed-forwarding the data. The framework has the interesting characteristic that no cluster centers need to be explicitly specified, thus the resulting cluster distribution is purely data-driven and no distance metrics need to be predefined. The experiments show that the proposed approach beats the conventional two-stage method (feature embedding with k-means) by a significant margin. It also compares favorably to the performance of the standard cross entropy loss for classification. Robustness analysis also shows that the method is largely insensitive to the number of clusters. Specifically, we show that the number of dominant clusters is close to the true number of clusters even when a large k is used for clustering.

Connected through hyperedge HE-1 (induced by source paper "multi class classification from noisy similarity labeled data"):
  with members:
    - "deep self learning from noisy labels"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "are anchor points really indispensable in label noise learning"
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "noise tolerant paradigm for training face recognition cnns"
    - "multi class classification without multi class labels"

Connected through hyperedge HE-2 (induced by source paper "multi class classification without multi class labels"):
  with members:
    - "classification from pairwise similarity and unlabeled data"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "visualizing the loss landscape of neural nets"
    - "learning to cluster in order to transfer across domains and tasks"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "deep constrained clustering algorithms and advances"):
  with members:
    - "probabilistic formulations of regression with mixed guidance"
    - "hierarchical clustering with structural constraints"
    - "clustering driven deep embedding with pairwise constraints"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "a framework for deep constrained clustering algorithms and advances"):
  with members:
    - "probabilistic formulations of regression with mixed guidance"
    - "hierarchical clustering with structural constraints"
    - "clustering driven deep embedding with pairwise constraints"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "facenet a unified embedding for face recognition and clustering"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-5 (induced by source paper "learning to cluster for proposal free instance segmentation"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "semantic instance segmentation with a discriminative loss function"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "object detection free instance segmentation with labeling transformations"

Connected through hyperedge HE-6 (induced by source paper "clustering oriented representation learning with attractive repulsive loss"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "clustering driven deep embedding with pairwise constraints"
    - "resolving event coreference with supervised representation learning and clustering oriented regularization"
    - "triplet center loss for multi view 3d object retrieval"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "low shot learning with imprinted weights"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-7 (induced by source paper "clustering driven deep embedding with pairwise constraints"):
  with members:
    - "learning representations and generative models for 3d point clouds"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "shapenet an information rich 3d model repository"
    - "unsupervised deep embedding for clustering analysis"
    - "semi supervised learning with ladder networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "semi supervised clustering with neural networks"):
  with members:
    - "clustering driven deep embedding with pairwise constraints"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "deep metric learning via facility location"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised deep embedding for clustering analysis"
    - "semi supervised learning with ladder networks"
    - "semi supervised learning with deep generative models"

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)