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: "on the efficacy of knowledge distillation"
  Abstract: In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures. Starting with the observation that more accurate teachers often don't make good teachers, we attempt to tease apart the factors that affect knowledge distillation performance. We find crucially that larger models do not often make better teachers. We show that this is a consequence of mismatched capacity, and that small students are unable to mimic large teachers. We find typical ways of circumventing this (such as performing a sequence of knowledge distillation steps) to be ineffective. Finally, we show that this effect can be mitigated by stopping the teacher's training early. Our results generalize across datasets and models.

Connected through hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-2 (induced by source paper "knowledge distillation via adaptive instance normalization"):
  with members:
    - "relational knowledge distillation"
    - "adaptis adaptive instance selection network"
    - "few shot adversarial learning of realistic neural talking head models"
    - "xnor net improved binary neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "pacemaker intermediate teacher knowledge distillation for on the fly convolutional neural network"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "yolo9000 better faster stronger"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-4 (induced by source paper "knowledge transfer graph for deep collaborative learning"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "random search and reproducibility for neural architecture search"
    - "relational knowledge distillation"
    - "dual student breaking the limits of the teacher in semi supervised learning"
    - "deeply supervised knowledge synergy"
    - "massively parallel hyperparameter tuning"
    - "knowledge distillation by on the fly native ensemble"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-5 (induced by source paper "quest quantized embedding space for transferring knowledge"):
  with members:
    - "relational knowledge distillation"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "revisit knowledge distillation a teacher free framework"
    - "similarity preserving knowledge distillation"
    - "paraphrasing complex network network compression via factor transfer"
    - "learning deep representations with probabilistic knowledge transfer"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"

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)