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: "cotype joint extraction of typed entities and relations with knowledge bases"
  Abstract: Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "joint embedding in named entity linking on sentence level"):
  with members:
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "effective attention modeling for neural relation extraction"
    - "end to end neural relation extraction using deep biaffine attention"
    - "attention guided graph convolutional networks for relation extraction"
    - "a question focused multi factor attention network for question answering"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "deep graph convolutional encoders for structured data to text generation"

Connected through hyperedge HE-4 (induced by source paper "neural architectures for open type relation argument extraction"):
  with members:
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "zero shot relation extraction via reading comprehension"
    - "reading wikipedia to answer open domain questions"
    - "pointing the unknown words"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "squad 100 000 questions for machine comprehension of text"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "joint extraction of entities and relations based on a novel decomposition strategy"):
  with members:
    - "porous lattice based transformer encoder for chinese ner"
    - "entity relation extraction as multi turn question answering"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "sentiment tagging with partial labels using modular architectures"
    - "adversarial training for multi context joint entity and relation extraction"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "heterogeneous supervision for relation extraction a representation learning approach"

Connected through hyperedge HE-6 (induced by source paper "joint extraction of entities and relations with a hierarchical multi task tagging model"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "design challenges and misconceptions in neural sequence labeling"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "semi supervised multitask learning for sequence labeling"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "creative procedural knowledge extraction from web design tutorials"):
  with members:
    - "synthesizing programs for images using reinforced adversarial learning"
    - "get to the point summarization with pointer generator networks"
    - "universal style transfer via feature transforms"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "bag of tricks for efficient text classification"
    - "colorful image colorization"
    - "scribbler controlling deep image synthesis with sketch and color"

Connected through hyperedge HE-8 (induced by source paper "doc2edag an end to end document level framework for chinese financial event extraction"):
  with members:
    - "one for all neural joint modeling of entities and events"
    - "fonduer knowledge base construction from richly formatted data"
    - "event extraction with generative adversarial imitation learning"
    - "reinforcement learning for relation classification from noisy data"
    - "diag nre a neural pattern diagnosis framework for distantly supervised neural relation extraction"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "joint extraction of events and entities within a document context"

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)