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: "large scale distributed semi supervised learning using streaming approximation"
  Abstract: Traditional graph-based semi-supervised learning (SSL) approaches, even though widely applied, are not suited for massive data and large label scenarios since they scale linearly with the number of edges $|E|$ and distinct labels $m$. To deal with the large label size problem, recent works propose sketch-based methods to approximate the distribution on labels per node thereby achieving a space reduction from $O(m)$ to $O(\log m)$, under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that captures the sparsity of the label distribution and ensures the algorithm propagates labels accurately, and further reduces the space complexity per node to $O(1)$. We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. We also study different graph construction mechanisms for natural language applications and propose a robust graph augmentation strategy trained using state-of-the-art unsupervised deep learning architectures that yields further significant quality gains.

Connected through hyperedge HE-1 (induced by source paper "graph rise graph regularized image semantic embedding"):
  with members:
    - "sampling matters in deep embedding learning"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "in datacenter performance analysis of a tensor processing unit"
    - "the inaturalist species classification and detection dataset"
    - "youtube 8m a large scale video classification benchmark"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "a survey on data collection for machine learning a big data ai integration perspective"):
  with members:
    - "data cleaning for accurate fair and robust models a big data ai integration approach"
    - "the gan landscape losses architectures regularization and normalization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "data synthesis based on generative adversarial networks"
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "a survey on deep transfer learning"
    - "learning based video motion magnification"

Connected through hyperedge HE-3 (induced by source paper "machine learning for heterogeneous ultra dense networks with graphical representations"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "a survey on applications of model free strategy learning in cognitive wireless networks"
    - "learning to decode linear codes using deep learning"
    - "scalable uplink signal detection in c rans via randomized gaussian message passing"

Connected through hyperedge HE-4 (induced by source paper "projectionnet learning efficient on device deep networks using neural projections"):
  with members:
    - "neural graph machines learning neural networks using graphs"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "smart reply automated response suggestion for email"
    - "revisiting semi supervised learning with graph embeddings"
    - "learning to hash for indexing big data a survey"
    - "compressing neural networks with the hashing trick"
    - "neural machine translation by jointly learning to align and translate"
    - "compressing deep convolutional networks using vector quantization"

Connected through hyperedge HE-5 (induced by source paper "bootstrapped graph diffusions exposing the power of nonlinearity"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "semi supervised learning on graphs through reach and distance diffusion"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "deep convolutional networks on graph structured data"
    - "diffusion convolutional neural networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "neural graph machines learning neural networks using graphs"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "smart reply automated response suggestion for email"
    - "revisiting semi supervised learning with graph embeddings"
    - "how transferable are features in deep neural networks"
    - "semi supervised learning with deep generative models"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "soft seeded ssl graphs for unsupervised semantic similarity based retrieval"):
  with members:
    - "smart reply automated response suggestion for email"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "inferring individual attributes from search engine queries and auxiliary information"):
  with members:
    - "learning about individuals from group statistics"

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)