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: "face to bmi using computer vision to infer body mass index on social media"
  Abstract: A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming" and other forms of "sizeism" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.

Connected through hyperedge HE-1 (induced by source paper "large scale and high resolution analysis of food purchases and health outcomes"):
  with members:
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "market basket prediction using user centric temporal annotated recurring sequences"
    - "fetishizing food in digital age foodporn around the world"
    - "kissing cuisines exploring worldwide culinary habits on the web"
    - "crowdsourcing health labels inferring body weight from profile pictures"
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-2 (induced by source paper "fooling computer vision into inferring the wrong body mass index"):
  with members:
    - "synthesizing robust adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "ordinal regression using noisy pairwise comparisons for body mass index range estimation"):
  with members:
    - "signet convolutional siamese network for writer independent offline signature verification"
    - "maximum selection and ranking under noisy comparisons"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "learning to compare image patches via convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "what does explainable ai really mean a new conceptualization of perspectives"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "can you tell where in india i am from comparing humans and computers on fine grained race face classification"
    - "image captioning with semantic attention"
    - "the mythos of model interpretability"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep visual semantic alignments for generating image descriptions"

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.HC(Human-Computer Interaction)