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: "deeptype multilingual entity linking by neural type system evolution"
  Abstract: The wealth of structured (e.g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence. So far, integration of these two different modalities is a difficult process, involving many decisions concerning how best to represent the information so that it will be captured or useful, and hand-labeling large amounts of data. DeepType overcomes this challenge by explicitly integrating symbolic information into the reasoning process of a neural network with a type system. First we construct a type system, and second, we use it to constrain the outputs of a neural network to respect the symbolic structure. We achieve this by reformulating the design problem into a mixed integer problem: create a type system and subsequently train a neural network with it. In this reformulation discrete variables select which parent-child relations from an ontology are types within the type system, while continuous variables control a classifier fit to the type system. The original problem cannot be solved exactly, so we propose a 2-step algorithm: 1) heuristic search or stochastic optimization over discrete variables that define a type system informed by an Oracle and a Learnability heuristic, 2) gradient descent to fit classifier parameters. We apply DeepType to the problem of Entity Linking on three standard datasets (i.e. WikiDisamb30, CoNLL (YAGO), TAC KBP 2010) and find that it outperforms all existing solutions by a wide margin, including approaches that rely on a human-designed type system or recent deep learning-based entity embeddings, while explicitly using symbolic information lets it integrate new entities without retraining.

Connected through hyperedge HE-1 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "end to end neural entity linking"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

Connected through hyperedge HE-2 (induced by source paper "multi step entity centric information retrieval for multi hop question answering"):
  with members:
    - "passage re ranking with bert"
    - "investigating the successes and failures of bert for passage re ranking"
    - "a deep look into neural ranking models for information retrieval"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "entity duet neural ranking understanding the role of knowledge graph semantics in neural information retrieval"
    - "the web as a knowledge base for answering complex questions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "latte latent type modeling for biomedical entity linking"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "improving entity linking by modeling latent relations between mentions"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-4 (induced by source paper "fine grained entity typing for domain independent entity linking"):
  with members:
    - "learning to denoise distantly labeled data for entity typing"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"
    - "named entity disambiguation for noisy text"

Connected through hyperedge HE-5 (induced by source paper "question answering based clinical text structuring using pre trained language model"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "deep contextualized word representations"
    - "neural semi markov conditional random fields for robust character based part of speech tagging"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast and accurate recognition of chinese clinical named entities with residual dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "zero shot entity linking with dense entity retrieval"):
  with members:
    - "learning dense representations for entity retrieval"
    - "fine grained entity typing for domain independent entity linking"
    - "zero shot entity linking by reading entity descriptions"
    - "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring"
    - "pytorch biggraph a large scale graph embedding system"
    - "neural collective entity linking"
    - "scalable graph based individual named entity identification"
    - "end to end neural entity linking"

Connected through hyperedge HE-7 (induced by source paper "simultaneously linking entities and extracting relations from biomedical text without mention level supervision"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "improving entity linking by modeling latent relations between mentions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "deep joint entity disambiguation with local neural attention"
    - "generalizing to unseen entities and entity pairs with row less universal schema"

Connected through hyperedge HE-8 (induced by source paper "improving distantly supervised entity typing with compact latent space clustering"):
  with members:
    - "a unified labeling approach by pooling diverse datasets for entity typing"
    - "semi supervised learning via compact latent space clustering"
    - "collective learning from diverse datasets for entity typing in the wild"
    - "attention is all you need"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "corpus level fine grained entity typing using contextual information"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"

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.CL(Computation and Language)