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: "are few shot learning benchmarks too simple"
  Abstract: We argue that the widely used Omniglot and miniImageNet benchmarks are too simple because their *class semantics* do not vary across episodes, which defeats their intended purpose of evaluating few-shot classification methods. The class semantics of Omniglot is invariably "characters" and the class semantics of miniImageNet, "object category". Because the class semantics are so similar, we propose a new method called Centroid Networks which can achieve surprisingly high accuracies on Omniglot and miniImageNet without using any labels at meta-evaluation time. Our results suggest that those benchmarks are not adapted for supervised few-shot classification since the supervision itself is not necessary during meta-evaluation. The Meta-Dataset, a collection of 10 datasets, was recently proposed as a harder few-shot classification benchmark. Using our method, we derive a new metric, the Class Semantics Consistency Criterion, and use it to quantify the difficulty of Meta-Dataset. Finally, under some restrictive assumptions, we show that Centroid Networks is faster and more accurate than a state-of-the-art learning-to-cluster method (Hsu et al 2018).

Connected through hyperedge HE-1 (induced by source paper "a robot that learns connect four using game theory and demonstrations"):
  with members:
    - "few shot learning with meta metric learners"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "meta learning for semi supervised few shot classification"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning game representations from data using rationality constraints"

Connected through hyperedge HE-2 (induced by source paper "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bilinear attention networks"

Connected through hyperedge HE-3 (induced by source paper "deep amortized clustering"):
  with members:
    - "multi class classification without multi class labels"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "clustering with deep learning taxonomy and new methods"
    - "conditional neural processes"
    - "deep sets"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "user experience of the costar system for instruction of collaborative robots"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "learning to discover novel visual categories via deep transfer clustering"):
  with members:
    - "multi class classification without multi class labels"
    - "semi supervised learning with scarce annotations"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "deep visual domain adaptation a survey"
    - "a survey on deep transfer learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "prototypical networks for few shot learning"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"

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)