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: "multi perspective relevance matching with hierarchical convnets for social media search"
  Abstract: Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.

Connected through hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "simple attention based representation learning for ranking short social media posts"):
  with members:
    - "deep relevance ranking using enhanced document query interactions"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "making neural qa as simple as possible but not simpler"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "cedr contextualized embeddings for document ranking"
    - "end to end open domain question answering with"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "neural vector spaces for unsupervised information retrieval"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "matchzoo a toolkit for deep text matching"
    - "neural models for information retrieval"

Connected through hyperedge HE-4 (induced by source paper "simple applications of bert for ad hoc document retrieval"):
  with members:
    - "passage re ranking with bert"
    - "simple attention based representation learning for ranking short social media posts"
    - "end to end open domain question answering with"
    - "a deep look into neural ranking models for information retrieval"
    - "evaluating sentence level relevance feedback for high recall information retrieval"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"

Connected through hyperedge HE-5 (induced by source paper "co stack residual affinity networks with multi level attention refinement for matching text sequences"):
  with members:
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a continuously growing dataset of sentential paraphrases"
    - "natural language inference over interaction space"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "neural paraphrase identification of questions with noisy pretraining"

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)