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: "what are the biases in my word embedding"
  Abstract: This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embeddings, including a supposedly "debiased" embedding. These embedded biases are concerning in light of the widespread use of word embeddings. The associations are identified by geometric patterns in word embeddings that run parallel between people's names and common lower-case words and phrases. The algorithm is highly unsupervised: it does not even require the sensitive groups (such as gender or race) to be pre-specified. This is desirable because it may not always be easy to identify all vulnerable groups a priori, and because it makes it easier to identify biases against intersectional groups, which depend on combinations of sensitive features. The inputs to our algorithm are a list of target tokens, e.g. names, and a word embedding, and the outputs are a number of Word Embedding Association Tests (WEATs) that capture various biases present in the data. We illustrate the utility of our approach on publicly available word embeddings and lists of names, and evaluate its output using crowdsourcing. We also show how removing names may not remove potential proxy bias.

Connected through hyperedge HE-1 (induced by source paper "mark my word a sequence to sequence approach to definition modeling"):
  with members:
    - "distributional semantics and linguistic theory"
    - "transformer xl attentive language models beyond a fixed length context"
    - "incorporating sememes into chinese definition modeling"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "scaling neural machine translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-2 (induced by source paper "measuring bias in contextualized word representations"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "gender bias in contextualized word embeddings"
    - "entity centric contextual affective analysis"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "gender bias in coreference resolution"
    - "feature wise bias amplification"

Connected through hyperedge HE-3 (induced by source paper "what s in a name reducing bias in bios without access to protected attributes"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "mitigating unwanted biases with adversarial learning"
    - "proxy fairness"
    - "semantics derived automatically from language corpora contain human like biases"
    - "equality of opportunity in supervised learning"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "began boundary equilibrium generative adversarial networks"
    - "examining cnn representations with respect to dataset bias"

Connected through hyperedge HE-5 (induced by source paper "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "unrealcv connecting computer vision to unreal engine"

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)