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: "ranking distillation learning compact ranking models with high performance for recommender system"
  Abstract: We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image recognition to achieve both effectiveness and efficiency. We propose a KD technique for learning to rank problems, called \emph{ranking distillation (RD)}. Specifically, we train a smaller student model to learn to rank documents/items from both the training data and the supervision of a larger teacher model. The student model achieves a similar ranking performance to that of the large teacher model, but its smaller model size makes the online inference more efficient. RD is flexible because it is orthogonal to the choices of ranking models for the teacher and student. We address the challenges of RD for ranking problems. The experiments on public data sets and state-of-the-art recommendation models showed that RD achieves its design purposes: the student model learnt with RD has a model size less than half of the teacher model while achieving a ranking performance similar to the teacher model and much better than the student model learnt without RD.

Connected through hyperedge HE-1 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite"):
  with members:
    - "mlperf training benchmark"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "understanding and improving knowledge distillation"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "when does label smoothing help"
    - "revisit knowledge distillation a teacher free framework"
    - "bag of tricks for image classification with convolutional neural networks"
    - "knowledge transfer with jacobian matching"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "large scale distributed neural network training through online distillation"
    - "heated up softmax embedding"

Connected through hyperedge HE-3 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning"):
  with members:
    - "micro and macro level churn analysis of large scale mobile games"
    - "joint neural collaborative filtering for recommender systems"
    - "value aware recommendation based on reinforced profit maximization in e commerce systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"
    - "ranking for relevance and display preferences in complex presentation layouts"

Connected through hyperedge HE-4 (induced by source paper "privileged features distillation at taobao recommendations"):
  with members:
    - "deep learning under privileged information using heteroscedastic dropout"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "large scale distributed neural network training through online distillation"
    - "self attentive sequential recommendation"
    - "deep mutual learning"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-5 (induced by source paper "aibench an industry standard internet service ai benchmark suite"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "towards neural mixture recommender for long range dependent user sequences"):
  with members:
    - "a simple convolutional generative network for next item recommendation"
    - "top k off policy correction for a reinforce recommender system"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a brand level ranking system with the customized attention gru model"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "session aware information embedding for e commerce product recommendation"
    - "contextual sequence modeling for recommendation with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "candidate generation with binary codes for large scale top n recommendation"):
  with members:
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "discrete factorization machines for fast feature based recommendation"
    - "deep discrete supervised hashing"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "the case for learned index structures"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "asymmetric deep supervised hashing"

Connected through hyperedge HE-8 (induced by source paper "collaborative distillation for top n recommendation"):
  with members:
    - "knowledge transfer with jacobian matching"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "triple generative adversarial nets"
    - "training shallow and thin networks for acceleration via knowledge distillation with conditional adversarial networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

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)