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: "automatic synonym discovery with knowledge bases"
  Abstract: Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "place deduplication with embeddings"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "billion scale similarity search with gpus"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "advances in pre training distributed word representations"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "a deep relevance matching model for ad hoc retrieval"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-2 (induced by source paper "probabilistic logic neural networks for reasoning"):
  with members:
    - "reasoning visual dialogs with structural and partial observations"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gmnn graph markov neural networks"
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "improving knowledge graph embedding using simple constraints"
    - "learning human object interactions by graph parsing neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"

Connected through hyperedge HE-3 (induced by source paper "surfcon synonym discovery on privacy aware clinical data"):
  with members:
    - "synonymnet multi context bilateral matching for entity synonyms"
    - "mining entity synonyms with efficient neural set generation"
    - "attention is all you need"
    - "semi supervised embedding in attributed networks with outliers"
    - "enriching word vectors with subword information"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "charagram embedding words and sentences via character n grams"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "learning dual retrieval module for semi supervised relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "dual supervised learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "self ensembling for visual domain adaptation"
    - "question answering and question generation as dual tasks"
    - "incorporating relation paths in neural relation extraction"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-5 (induced by source paper "synonymnet multi context bilateral matching for entity synonyms"):
  with members:
    - "deep contextualized word representations"
    - "bilateral multi perspective matching for natural language sentences"
    - "multi perspective context matching for machine comprehension"
    - "charagram embedding words and sentences via character n grams"
    - "a compare aggregate model for matching text sequences"
    - "lstm based deep learning models for non factoid answer selection"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "mining entity synonyms with efficient neural set generation"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "deep sets"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "learning to cluster in order to transfer across domains and tasks"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "solving verbal comprehension questions in iq test by knowledge powered word embedding"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-7 (induced by source paper "finding average regret ratio minimizing set in database"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised relation extraction by pattern enhanced embedding learning"):
  with members:
    - "metapad meta pattern discovery from massive text corpora"
    - "incorporating relation paths in neural relation extraction"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network 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)