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: "label noise reduction in entity typing by heterogeneous partial label embedding"
  Abstract: Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, the type labels so obtained from knowledge bases are often noisy (i.e., incorrect for the entity mention's local context). We define a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic identification of correct type labels (type-paths) for training examples, given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity between entity types pose unique challenges for solving the LNR task. We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type-path for each training example in a top-down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.

Connected through hyperedge HE-1 (induced by source paper "fine grained entity typing in hyperbolic space"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "hyperbolic attention networks"
    - "skip gram word embeddings in hyperbolic space"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "ultra fine entity typing"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-2 (induced by source paper "dynamic network embedding via incremental skip gram with negative sampling"):
  with members:
    - "learning structural node embeddings via diffusion wavelets"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-3 (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"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "imposing label relational inductive bias for extremely fine grained entity typing"):
  with members:
    - "neural fine grained entity type classification with hierarchy aware loss"
    - "ultra fine entity typing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "fine grained entity typing with high multiplicity assignments"
    - "learned in translation contextualized word vectors"
    - "enhanced lstm for natural language inference"
    - "neural architectures for fine grained entity type classification"
    - "an attentive neural architecture for fine grained entity type classification"

Connected through hyperedge HE-5 (induced by source paper "learning to denoise distantly labeled data for entity typing"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-6 (induced by source paper "fine grained entity recognition with reduced false negatives and large type coverage"):
  with members:
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "corpus level fine grained entity typing"
    - "neural architectures for fine grained entity type classification"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "polyglot ner massive multilingual named entity recognition"
    - "context dependent fine grained entity type tagging"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "improving fine grained entity typing with entity linking"):
  with members:
    - "improving distantly supervised entity typing with compact latent space clustering"
    - "ultra fine entity typing"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "deep joint entity disambiguation with local neural attention"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "building a fine grained entity typing system overnight for a new x x language domain genre"
    - "neural architectures for fine grained entity type classification"
    - "context dependent fine grained entity type tagging"

Connected through hyperedge HE-8 (induced by source paper "kcat a knowledge constraint typing annotation tool"):
  with members:
    - "learning to denoise distantly labeled data for entity typing"
    - "improving entity linking by modeling latent relations between mentions"
    - "ultra fine entity typing"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "deep joint entity disambiguation with local neural attention"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "context dependent fine grained entity type tagging"
    - "introduction to the conll 2000 shared task chunking"

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)