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: "efficient and effective spam filtering and re ranking for large web datasets"
  Abstract: The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general Web in early 2009. This dataset contains 1 billion web pages, a substantial fraction of which are spam --- pages designed to deceive search engines so as to deliver an unwanted payload. We examine the effect of spam on the results of the TREC 2009 web ad hoc and relevance feedback tasks, which used the ClueWeb09 dataset. We show that a simple content-based classifier with minimal training is efficient enough to rank the "spamminess" of every page in the dataset using a standard personal computer in 48 hours, and effective enough to yield significant and substantive improvements in the fixed-cutoff precision (estP10) as well as rank measures (estR-Precision, StatMAP, MAP) of nearly all submitted runs. Moreover, using a set of "honeypot" queries the labeling of training data may be reduced to an entirely automatic process. The results of classical information retrieval methods are particularly enhanced by filtering --- from among the worst to among the best.

Connected through hyperedge HE-1 (induced by source paper "abstractive snippet generation"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "query based abstractive summarization using neural networks"
    - "learning paraphrastic sentence embeddings from back translated bitext"

Connected through hyperedge HE-2 (induced by source paper "a passage based approach to learning to rank documents"):
  with members:
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "neural ranking models with weak supervision"
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-3 (induced by source paper "the simplest thing that can possibly work pseudo relevance feedback using text classification"):
  with members:
    - "nprf a neural pseudo relevance feedback framework for ad hoc information retrieval"

Connected through hyperedge HE-4 (induced by source paper "ranking robustness under adversarial document manipulations"):
  with members:
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "robustness of classifiers from adversarial to random noise"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "the stochastic gradient descent for the primal l1 svm optimization revisited"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning to rank from samples of variable quality"):
  with members:
    - "neural ranking models with weak supervision"
    - "learning to attend copy and generate for session based query suggestion"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"

Connected through hyperedge HE-6 (induced by source paper "neural ranking models with weak supervision"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "relevance based word embedding"
    - "a deep relevance matching model for ad hoc retrieval"
    - "neural information retrieval a literature review"
    - "getting started with neural models for semantic matching in web search"
    - "query expansion with locally trained word embeddings"
    - "learning to match using local and distributed representations of text for web search"
    - "unsupervised efficient and semantic expertise retrieval"

Connected through hyperedge HE-7 (induced by source paper "relevance based word embedding"):
  with members:
    - "neural ranking models with weak supervision"
    - "query expansion with locally trained word embeddings"

Connected through hyperedge HE-8 (induced by source paper "rhetorical relations for information retrieval"):
  with members:
    - "laplace s method approximations for probabilistic inference in belief networks with continuous variables"

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.IR(Information Retrieval)