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: "learning determinantal point processes"
  Abstract: Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among many remarkable properties, DPPs offer tractable algorithms for exact inference, including computing marginal probabilities and sampling; however, an important open question has been how to learn a DPP from labeled training data. In this paper we propose a natural feature-based parameterization of conditional DPPs, and show how it leads to a convex and efficient learning formulation. We analyze the relationship between our model and binary Markov random fields with repulsive potentials, which are qualitatively similar but computationally intractable. Finally, we apply our approach to the task of extractive summarization, where the goal is to choose a small subset of sentences conveying the most important information from a set of documents. In this task there is a fundamental tradeoff between sentences that are highly relevant to the collection as a whole, and sentences that are diverse and not repetitive. Our parameterization allows us to naturally balance these two characteristics. We evaluate our system on data from the DUC 2003/04 multi-document summarization task, achieving state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "scoring sentence singletons and pairs for abstractive summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structure infused copy mechanisms for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"

Connected through hyperedge HE-4 (induced by source paper "video summarization via actionness ranking"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "temporal action detection with structured segment networks"
    - "video summarization with attention based encoder decoder networks"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"

Connected through hyperedge HE-5 (induced by source paper "towards diverse and accurate image captions via reinforcing determinantal point process"):
  with members:
    - "describing like humans on diversity in image captioning"
    - "discriminability objective for training descriptive captions"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-6 (induced by source paper "composable core sets for determinant maximization a simple near optimal algorithm"):
  with members:
    - "composable core sets for determinant maximization problems via spectral spanners"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "how to be fair and diverse"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "streaming algorithms for submodular function maximization"

Connected through hyperedge HE-7 (induced by source paper "multi document summarization with determinantal point processes and contextualized representations"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "structure infused copy mechanisms for abstractive summarization"

Connected through hyperedge HE-8 (induced by source paper "what comes next extractive summarization by next sentence prediction"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "neural summarization by extracting sentences and words"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "skip thought vectors"

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)