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: "learning to cluster in order to transfer across domains and tasks"
  Abstract: This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning. We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not. This similarity is category-agnostic and can be learned from data in the source domain using a similarity network. We then present two novel approaches for performing transfer learning using this similarity function. First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs. Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network. Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches. Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet. Our results show that we can reconstruct semantic clusters with high accuracy. We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets. Our approach doesn't explicitly deal with domain discrepancy. If we combine with a domain adaptation loss, it shows further improvement.

Connected through hyperedge HE-1 (induced by source paper "m adda unsupervised domain adaptation with deep metric learning"):
  with members:
    - "deep visual domain adaptation a survey"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "self ensembling for visual domain adaptation"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "from source to target and back symmetric bi directional adaptive gan"

Connected through hyperedge HE-2 (induced by source paper "set2graph learning graphs from sets"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "meta learning to cluster"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on universal equivariant set networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "clustering with deep learning taxonomy and new methods"

Connected through hyperedge HE-3 (induced by source paper "automatically discovering and learning new visual categories with ranking statistics"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "multi class classification without multi class labels"
    - "semi supervised learning with scarce annotations"
    - "revisiting self supervised visual representation learning"
    - "a survey on deep transfer learning"
    - "unsupervised representation learning by predicting image rotations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-4 (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-5 (induced by source paper "charting the right manifold manifold mixup for few shot learning"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-6 (induced by source paper "large scale long tailed recognition in an open world"):
  with members:
    - "model cards for model reporting"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "women also snowboard overcoming bias in captioning models"
    - "arcface additive angular margin loss for deep face recognition"
    - "episodic curiosity through reachability"
    - "incremental few shot learning with attention attractor networks"
    - "low shot learning from imaginary data"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-7 (induced by source paper "temporal attentive alignment for large scale video domain adaptation"):
  with members:
    - "learning transferable self attentive representations for action recognition in untrimmed videos with weak supervision"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "learning correspondence from the cycle consistency of time"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "open set domain adaptation by backpropagation"
    - "a short note about kinetics 600"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "domain generalization via model agnostic learning of semantic features"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "on first order meta learning algorithms"
    - "learning to adapt structured output space for semantic segmentation"
    - "generalizing across domains via cross gradient training"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "semi supervised learning via compact latent space 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.LG(Machine Learning)