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: "scaling graph based semi supervised learning to large number of labels using count min sketch"
  Abstract: Graph-based Semi-supervised learning (SSL) algorithms have been successfully used in a large number of applications. These methods classify initially unlabeled nodes by propagating label information over the structure of graph starting from seed nodes. Graph-based SSL algorithms usually scale linearly with the number of distinct labels (m), and require O(m) space on each node. Unfortunately, there exist many applications of practical significance with very large m over large graphs, demanding better space and time complexity. In this paper, we propose MAD-SKETCH, a novel graph-based SSL algorithm which compactly stores label distribution on each node using Count-min Sketch, a randomized data structure. We present theoretical analysis showing that under mild conditions, MAD-SKETCH can reduce space complexity at each node from O(m) to O(log m), and achieve similar savings in time complexity as well. We support our analysis through experiments on multiple real world datasets. We observe that MAD-SKETCH achieves similar performance as existing state-of-the-art graph- based SSL algorithms, while requiring smaller memory footprint and at the same time achieving up to 10x speedup. We find that MAD-SKETCH is able to scale to datasets with one million labels, which is beyond the scope of existing graph- based SSL algorithms.

Connected through hyperedge HE-1 (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-2 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "reverse ranking by graph structure model and scalable algorithms"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

Connected through hyperedge HE-3 (induced by source paper "bootstrapping distantly supervised ie using joint learning and small well structured corpora"):
  with members:
    - "distant ie by bootstrapping using lists and document structure"

Connected through hyperedge HE-4 (induced by source paper "morpho syntactic lexicon generation using graph based semi supervised learning"):
  with members:
    - "an unsupervised method for uncovering morphological chains"
    - "a universal part of speech tagset"
    - "multi label prediction via compressed sensing"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-5 (induced by source paper "large scale distributed semi supervised learning using streaming approximation"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "on information regularization"

Connected through hyperedge HE-6 (induced by source paper "joint inference of multiple label types in large networks"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "sparse projections onto the simplex"
    - "an infinite latent attribute model for network data"

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)