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: "fine grained activity recognition with holistic and pose based features"
  Abstract: Holistic methods based on dense trajectories are currently the de facto standard for recognition of human activities in video. Whether holistic representations will sustain or will be superseded by higher level video encoding in terms of body pose and motion is the subject of an ongoing debate. In this paper we aim to clarify the underlying factors responsible for good performance of holistic and pose-based representations. To that end we build on our recent dataset leveraging the existing taxonomy of human activities. This dataset includes 24,920 video snippets covering 410 human activities in total. Our analysis reveals that holistic and pose-based methods are highly complementary, and their performance varies significantly depending on the activity. We find that holistic methods are mostly affected by the number and speed of trajectories, whereas pose-based methods are mostly influenced by viewpoint of the person. We observe striking performance differences across activities: for certain activities results with pose-based features are more than twice as accurate compared to holistic features, and vice versa. The best performing approach in our comparison is based on the combination of holistic and pose-based approaches, which again underlines their complementarity.

Connected through hyperedge HE-1 (induced by source paper "turbo learning framework for human object interactions recognition and human pose estimation"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "detecting and recognizing human object interactions"
    - "human pose regression by combining indirect part detection and contextual information"
    - "learning to detect human object interactions"
    - "knowledge guided deep fractal neural networks for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"

Connected through hyperedge HE-2 (induced by source paper "adaptive feature processing for robust human activity recognition on a novel multi modal dataset"):
  with members:
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "every moment counts dense detailed labeling of actions in complex videos"
    - "histogram of oriented principal components for cross view action recognition"

Connected through hyperedge HE-3 (induced by source paper "multimodal explanations justifying decisions and pointing to the evidence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpreting deep visual representations via network dissection"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "hadamard product for low rank bilinear pooling"
    - "spice semantic propositional image caption evaluation"
    - "learning what and where to draw"
    - "generating visual explanations"

Connected through hyperedge HE-4 (induced by source paper "crowdpose efficient crowded scenes pose estimation and a new benchmark"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-5 (induced by source paper "loss guided activation for action recognition in still images"):
  with members:
    - "detecting and recognizing human object interactions"
    - "expanded parts model for semantic description of humans in still images"
    - "attentional pooling for action recognition"
    - "single image action recognition using semantic body part actions"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-6 (induced by source paper "detection tracking for efficient person analysis the detta pipeline"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "the strands project long term autonomy in everyday environments"
    - "mot16 a benchmark for multi object tracking"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "second order temporal pooling for action recognition"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-8 (induced by source paper "generating descriptions with grounded and co referenced people"):
  with members:
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "grounding of textual phrases in images by reconstruction"
    - "beyond caption to narrative video captioning with multiple sentences"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"
    - "contextual action recognition with r cnn"
    - "learning deep structure preserving image text embeddings"

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.CV(Computer Vision and Pattern Recognition)