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: "the multiverse loss for robust transfer learning"
  Abstract: Deep learning techniques are renowned for supporting effective transfer learning. However, as we demonstrate, the transferred representations support only a few modes of separation and much of its dimensionality is unutilized. In this work, we suggest to learn, in the source domain, multiple orthogonal classifiers. We prove that this leads to a reduced rank representation, which, however, supports more discriminative directions. Interestingly, the softmax probabilities produced by the multiple classifiers are likely to be identical. Experimental results, on CIFAR-100 and LFW, further demonstrate the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "mml maximal multiverse learning for robust fine tuning of language models"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network acceptability judgments"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-2 (induced by source paper "multi task convolutional neural network for pose invariant face recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "towards large pose face frontalization in the wild"
    - "cross stitch networks for multi task learning"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "triplet probabilistic embedding for face verification and clustering"
    - "learning a metric embedding for face recognition using the multibatch method"
    - "deepid3 face recognition with very deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "leveraging class similarity to improve deep neural network robustness"):
  with members:
    - "focal loss for dense object detection"
    - "deep metric learning with angular loss"
    - "disturblabel regularizing cnn on the loss layer"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "supervised learning of universal sentence representations from natural language inference data"):
  with members:
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a structured self attentive sentence embedding"
    - "language modeling with gated convolutional networks"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "how transferable are neural networks in nlp applications"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-5 (induced by source paper "generalized rank pooling for activity recognition"):
  with members:
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "rank pooling for action recognition"
    - "ordered pooling of optical flow sequences for action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-6 (induced by source paper "latent hinge minimax risk minimization for inference from a small number of training samples"):
  with members:
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "lcnn lookup based convolutional neural network"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "matching networks for one shot learning"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "low shot visual object recognition"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-8 (induced by source paper "low shot visual recognition by shrinking and hallucinating features"):
  with members:
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning feed forward one shot learners"
    - "aga attribute guided augmentation"
    - "matching networks for one shot learning"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "prototypical priors from improving classification to zero shot learning"
    - "deep residual learning for image recognition"

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)